OptimizationSurvey/Results
Survey Results
On this page, we provide the complete data we extracted from the surveyed papers. First, a navigatable version of the taxonomy is available to browse through all our results. Second, the raw data is presented in two tables below.
Please click on the image below to be redirected to the navigatable taxonomy. Click on a taxonomy entry to see all possible values in the first column. Click on a value in that column to see all papers that use it in the second column. Click on a paper in the second column to see all details on the paper on the right hand side.
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Results without Comments
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| PaperID | Title | Domain | Dimen. | Phase | Quality attribute | Constraints | Constraint Handling | Quality Evalution | Optimisation problem class | Optimisation strategy I | Optimization Strategy II | Optimisation strategy III | Transformation operators | Approach Validation | Optimization validation | |
| Abdelzaher95S | Optimal Combined Task and Message Scheduling in Distributed Real-Time Systems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
BRANCH AND BOUND, GREEDY |
SCHEDULING | EXPERIMENTS | INTERNAL COMPARISSON | |
| Abraham08LZ | Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | ALLOCATION, SCHEDULING |
NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Agarwal10AS | Optimal redundancy allocation in complex systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY, PERFORMANCE |
GENERAL, PROHIBIT, COST, WEIGHT |
PROHIBIT | GENERAL | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | SOFTWARE REPLICATION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Ardagna06GIMP | QoS-Driven Web Services Selection in Autonomic Grid Environments | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
TIMING, QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | SERVICE SELECTION | NOT PRESENTED | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Ardagna06P | Global and Local QoS Guarantee in Web Service Selection | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
TIMING, QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | SERVICE SELECTION | EXPERIMENTS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Ardagna07P | Adaptive Service Composition in Flexible Processes | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, REPUTATION |
STRUCTURAL, REQUIREMENTS, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Ardagna10M | Per-flow optimal service Selection for Web services based processes | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT STANDARD | SEQUENTIAL QUADRATIC PROGRAMMING | SERVICE SELECTION | EXPERIMENTS | NOT NEEDED, COMPARISON WITH BASELINE HEURISTIC ALGORITHM |
|
| Azaron09PKKS | Multi-objective Reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
GENERAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Balasubramanian10GDWLGS | A Model-driven QoS Provisioning Engine for Cyber Physical Systems | GENERAL | GENERAL | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | ALLOCATION | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Berbner06SRHS | Heuristics for QoS-aware Web Service Composition | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC, METAHEURISTIC |
OTHER PROBLEM SPECIFIC, SIMULATED ANNEALING, MIXED-INTEGER LINEAR PROGRAMMING (MILP) |
OTHER PROBLEM SPECIFIC | EXPERIMENTS | NOT PRESENTED | |
| Bhunia10SR | Reliability stochastic optimization for a series system with interval component reliability via genetic algorithm | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | GENERAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NOT PRESENTED | SIMPLE EXAMPLE | NOT PRESENTED | |
| Burmester08GMOKS | Tool support for the design of self-optimizing mechatronic multi-agent systems | EMBEDDED SYSTEMS | GENERAL | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | NOT APPLICABLE | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | COMPONENT SELECTION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Busacca01MZ | Multiobjective optimization by genetic algorithms: application to safety systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, HARDWARE SELECTION |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Canfora05DEV | An Approach for QoS-aware Service Composition based on Genetic Algorithms | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
GENERAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT PRESENTED | |
| Canfora06DEPV | Service Composition (re)Binding Driven by Application–Specific QoS | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Canfora08PEV | A Framework for QoS-Aware Binding and Re-Binding of CompositeWeb Services | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
GENERAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE COMPOSITION | ACADEMIC CASE STUDY | NOT PRESENTED | |
| Cao05CL | Genetic Algorithm Utilized in Cost-Reduction Driven Web Service Selection | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | COST | COST, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION | ACADEMIC CASE STUDY | NOT PRESENTED | |
| Cardellini06CGM | A Framework for Optimal Service Selection in Broker-based Architectures with Multiple QoS Classes | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
STABILITY, FUNCTIONAL CORRECTNESS, QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | SEQUENTIAL QUADRATIC PROGRAMMING | SERVICE SELECTION | ACADEMIC CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Cardellini07CGL | Flow-Based Service Selection forWeb Service Composition Supporting Multiple QoS Classes | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
ACADEMIC CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Cardellini09CGPM | QoS-driven Runtime Adaptation of Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Chang09CL | An ant algorithm for balanced job scheduling in grids | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | ALLOCATION, SCHEDULING |
INDUSTRIAL CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Chou95OB | Interface Co-Synthesis Techniques for Embedded Systems | EMBEDDED SYSTEMS | GENERAL | DESIGN-TIME | COST | PERFORMANCE, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | OTHER PROBLEM SPECIFIC | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Coelho07 | An efficient particle swarm approach for Mixed-Integer programming in Reliability–Redundancy optimization applications | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | VOLUME, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | PARTICLE SWARM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Coelho08 | Reliability–Redundancy optimization by means of a chaotic differential evolution approach | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, VOLUME, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
INDUSTRIAL CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Coit00L | SYSTEM Reliability OPTIMIZATION WITH k-out-of-n SUBSYSTEMS | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Coit01 | Cold-standby Redundancy optimization for nonRepairable systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | WEIGHT, PROHIBIT, COST |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR MIXED INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Coit01J | MULTI-CRITERIA OPTIMIZATION: MAXIMIZATION OF A SYSTEM Reliability ESTIMATE AND MINIMIZATION OF THE ESTIMATE VARIANCE | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | GENERAL, NOT PRESENTED |
NOT PRESENTED | GENERAL | NONLINEAR INTEGER | GENERAL | GENERAL | GENERAL | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Coit02S | Genetic algorithm to maximize a lower-bound for system time-to-failure with uncertain Component Weibull Parameters | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | GENERAL, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Coit03 | Maximization of system Reliability with a choice of Redundancy strategies | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Coit04JW | System Optimization With Component Reliability Estimation Uncertainty: A Multi-Criteria Approach | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | GENERAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Coit96Sa | SOLVING THE Redundancy Allocation PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM APPROACH | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | RELIABILITY, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Coit96Sb | Reliability Optimization of Series-Parallel Systems using a Genetic Algorithm | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Coit98S | Redundancy Allocation to Maximize a Lower Percentile of the System Time-to-Failure Distribution | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | GENERAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Cortellessa09P | How Can Optimization Models Support the Maintenance of Component-Based Software? | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | COST | DELIVERY TIME, RELIABILITY, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | COMPONENT SELECTION, OTHER PROBLEM SPECIFIC |
NOT PRESENTED | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Salazar07R | Solving advanced multi-objective robust designs by means of Multiple objective Evolutionary algorithms (MOEA): A Reliability application | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
COST, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, HARDWARE PARAMETERS |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Dipenta06EVCCD | WS Binder: a Framework to enable Dynamic Binding of Composite Web Services | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | GENERAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | OTHER PROBLEM SPECIFIC | ACADEMIC CASE STUDY | NOT PRESENTED | |
| Dogan01O | Biobjective Scheduling Algorithms for Execution Time-Reliability Trade-off in Heterogeneous Computing Systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
PRECEDENCE, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, GREEDY |
SCHEDULING | NOT PRESENTED | INTERNAL COMPARISSON | |
| Dong06Y | Optimizing Web Service Composition Based on QoS Negotiation | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
QOS VALUES, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NOT APPLICABLE | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT PRESENTED | |
| Dubey10M | Utility-based Optimal Service Selection for Business Processes in Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | HILL CLIMBING | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | COMPARISON WITH EXACT ALGORITHM | |
| ElHaddad10MR | TQoS: Transactional and QoS-aware Selection algorithm for automatic Web service composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, AVAILABILITY, PERFORMANCE, REPUTATION |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | NOT PRESENTED | |
| Erbas05CP | Multiobjective Optimization and Evolutionary Algorithms for the Application Mapping Problem in Multiprocessor System-on-Chip Design | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY, PERFORMANCE |
STRUCTURAL, REPAIR |
REPAIR | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | INDUSTRIAL CASE STUDY | INTERNAL COMPARISSON | |
| Etminani07N | A Min-Min Max-Min Selective Algorihtm for Grid Task Scheduling | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | SCHEDULING | NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Falco07DST | Multiobjective Differential Evolution for Mapping in a Grid Environment | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Giese03BKST | Multi-Agent System Design for Safety-Critical Self-Optimizing Mechatronic Systems with UML | EMBEDDED SYSTEMS | GENERAL | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | NOT APPLICABLE | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | SIMPLE EXAMPLE | NOT PRESENTED | |
| Giovanni10P | An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, SCHEDULING |
BENCHMARK PROBLEMS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Guo07HLDLD | ANGEL: Optimal Configuration for High Available Service Composition | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | AVAILABILITY | COST, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | SERVICE COMPOSITION | EXPERIMENTS | INTERNAL COMPARISSON | |
| Hadj-Alouanee96BM | A Hybrid Genetic/Optimization Algorithm for a Task Allocation Problem | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | PHYSICAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | NOT PRESENTED | COMPARISON WITH EXACT ALGORITHM | |
| He10GZ | Task Allocation and Optimization of Distributed Embedded Systems with Simulated Annealing and Geometric Programming | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | TIMING, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ALLOCATION, SCHEDULING |
EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Huang09QZ | Genetic-algorithm-based optimal apportionment of Reliability and Redundancy under Multiple objectives | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
VOLUME, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Huynh09M | Runtime Reconfiguration of Custom Instructions for Real-Time Embedded Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY, PERFORMANCE |
TIMING, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | DYNAMIC PROGRAMMING | SCHEDULING | SIMPLE EXAMPLE | COMPARISON WITH EXACT ALGORITHM | |
| Jafarpour10K | QoS-aware Selection ofWeb Service Composition QoS-aware Selection ofWeb Service Composition Based on Harmony Search Algorithm | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY, REPUTATION |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | HARMONY SEARCH | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | NOT PRESENTED | |
| Kaya09U | Exact algorithms for a task assignment problem | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | ALLOCATION | EXPERIMENTS | NOT NEEDED, COMPARISON WITH BASELINE HEURISTIC ALGORITHM |
|
| Kishor07YK | Application of a Multi-objective Genetic Algorithm to solve Reliability Optimization Problem | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NOT PRESENTED | SIMPLE EXAMPLE | NOT PRESENTED | |
| Ko08KK | Quality-of-service oriented web service composition algorithm and planning architecture | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
REDUNDANCY LEVEL, QOS VALUES, REPAIR |
REPAIR | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT PRESENTED | |
| Kokash07D | Evaluating Quality of Web Services: A Risk-Driven Approach | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | RELIABILITY | COST, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | SERVICE COMPOSITION | EXPERIMENTS | NOT PRESENTED | |
| Kulturel-Konak02SC | Efficiently solving the Redundancy Allocation problem using tabu search | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Kulturel-Konak07CB | Pruned Pareto-optimal sets for the system Redundancy Allocation problem based on Multiple prioritized objectives | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, WEIGHT |
NOT PRESENTED | NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Kunzli05TZ | A Modular Design Space Exploration Framework for Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | GENERAL, PENALTY |
PENALTY | GENERAL | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | ANY METAHEURISTICS | GENERAL | SIMPLE EXAMPLE | NOT PRESENTED | |
| Kunzli06 | Efficient Design Space Exploration for Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | GENERAL, PENALTY |
PENALTY | GENERAL | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | ANY METAHEURISTICS | GENERAL | ACADEMIC CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Laalaoui09DBA | Ant Colony System with Stagnation Avoidance For the Scheduling of Real-Time Tasks | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | TIMING, PRECEDENCE, PROHIBIT |
PROHIBIT | NOT PRESENTED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | SCHEDULING | NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Lee10KH | A Systematic Design Space Exploration of MPSoC Based on Synchronous Data Flow Specification | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | PERFORMANCE, REPAIR |
REPAIR | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | COMPONENT SELECTION, ALLOCATION, SCHEDULING |
INDUSTRIAL CASE STUDY, EXPERIMENTS |
COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Liang07LC | Variable neighbourhood search for Redundancy Allocation problems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION, MULTI-OBJECTIVE OPTIMIZATION |
DESIGN-TIME | RELIABILITY, COST |
COST, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, VARIABLE NEIGHBOURHOOD SEARCH |
HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Lukasiewycz10GT | Robust Design of Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | GENERAL | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | GENERAL | INDUSTRIAL CASE STUDY, EXPERIMENTS |
NOT PRESENTED | |
| Marseguerra06M | Basics of genetic algorithms optimization for RAMS applications | EMBEDDED SYSTEMS | GENERAL | DESIGN-TIME | AVAILABILITY, COST, MAINTAINABILITY, RELIABILITY, SAFETY |
DESIGN, PENALTY |
PENALTY | NOT PRESENTED | NOT APPLICABLE | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE PARAMETERS, HARDWARE REPLICATION, MAINTENANCE SCHEDULES |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Martorell04SCS | Alternatives and challenges in optimizing industrial safety using genetic algorithms | EMBEDDED SYSTEMS | GENERAL | DESIGN-TIME | AVAILABILITY, COST, MAINTAINABILITY, RELIABILITY, SAFETY |
DESIGN, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MAINTENANCE SCHEDULES | INDUSTRIAL CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Menasce10EGMS | A Framework for Utility-Based Service Oriented Design in SASSY | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, PERFORMANCE, SECURITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | HILL CLIMBING | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Naderi10GA | A high performing metaheuristic for job shop scheduling with sequence-dependent setup times | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | SCHEDULING | NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Nicholson96P | Design Synthesis Using Adaptive Search Techniques and Multi-Criteria Decision Analysis | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | COST, RELIABILITY, SAFETY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, COMPONENT SELECTION, ALLOCATION |
NOT PRESENTED | NOT PRESENTED | |
| Oh99H | A Hardware-Software Cosynthesis Technique Based on Heterogeneous Multiprocessor Scheduling | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | PERFORMANCE | PERFORMANCE, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION, SCHEDULING |
EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Ouzineb08NG | Tabu search for the Redundancy Allocation problem of homogenous series–parallel multi-state systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | AVAILABILITY, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION |
SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Ouzineb10NG | An efficient heuristic for reliability design optimization problems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PROHIBIT |
PROHIBIT | GENERAL | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, TABU SEARCH |
COMPONENT SELECTION | SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Painton95C | Genetic Algorithms in Optimization of System Reliability | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION |
SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Pimentel06EP | A Systematic Approach to Exploring Embedded System Architectures at Multiple Abstraction Levels | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY, PERFORMANCE |
STRUCTURAL, REPAIR |
REPAIR | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Pop09DC | Genetic Algorithm for DAG Scheduling in Grid Environments | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | TIMING, PRECEDENCE, PHYSICAL, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, SCHEDULING |
NOT PRESENTED | NOT PRESENTED | |
| Qin05J | A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | RELIABILITY | TIMING, PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | ALLOCATION, SCHEDULING |
NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Raiha08KM | Genetic Synthesis of Software Architecture | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | MODIFIABILITY, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ARCHITECTURAL PATTERN | ACADEMIC CASE STUDY, EXPERIMENTS |
NOT PRESENTED | |
| Raiha09KM | Scenario-Based Genetic Synthesis of Software Architecture | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | MODIFIABILITY, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ARCHITECTURAL PATTERN | ACADEMIC CASE STUDY | INTERNAL COMPARISSON | |
| Raiha09MP | Using simulated annealing for producing software architectures | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | MODIFIABILITY, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ARCHITECTURAL PATTERN | ACADEMIC CASE STUDY | INTERNAL COMPARISSON | |
| Rosenberg10MLMBD | MetaHeuristics Optimization of Large-Scale QoS-Aware Service Compositions MetaHeuristics Optimization of Large-Scale | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING |
SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | NOT PRESENTED | |
| Roshanaei09NJK | A variable neighborhood search for job shop scheduling with set-up times to minimize makespan | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | ALLOCATION, SCHEDULING |
NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Salazar06RG | Optimization of constrained Multiple-objective Reliability problems using Evolutionary algorithms | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Shan08W | Reliable design space and complete single-loop Reliability-based design optimization | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | GENERAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR CONTINOUS | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | GENERAL | MATHEMATICAL PROOF, SIMPLE EXAMPLE |
NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Sharma09A | Ant Colony Optimization Approach to Heterogeneous Redundancy in Multi-state Systems with Multi-state Components | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | RELIABILITY, WEIGHT, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | COMPONENT SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Stuijk07BGC | Multiprocessor Resource Allocation for ThroughputConstrained Synchronous Data ow Graphs | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | THROUGHPUT, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION, SCHEDULING |
BENCHMARK PROBLEMS | NOT PRESENTED | |
| Taboada06BC | Practical solutions for multi-objective optimization: An application to system Reliability design problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, WEIGHT |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Taboada06Ca | Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, WEIGHT |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Taboada08EC | MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST, WEIGHT |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
EXPERIMENTS | NOT PRESENTED | |
| Tian09LZ | A joint Reliability–Redundancy optimization approach for multi-state series–parallel systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | AVAILABILITY, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Tindell92BW | Allocating Hard Real Time Tasks (An NP-Hard Problem Made Easy) | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | PERFORMANCE | TIMING, PHYSICAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ALLOCATION, SCHEDULING |
SIMPLE EXAMPLE | COMPARISON WITH EXACT ALGORITHM | |
| Vanrompay08RB | Genetic Algorithm-Based Optimization of Service Composition and Deployment | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
MEMORY, PROCESSING POWER, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE COMPOSITION | NOT PRESENTED | NOT PRESENTED | |
| Wada08CSO | Multiobjective Optimization of SLA-aware Service Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE COMPOSITION | EXPERIMENTS | NOT PRESENTED | |
| Wang03GK | A New Approach for Task Level Computational Resource Bipartitioning | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | COST, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | ALLOCATION | BENCHMARK PROBLEMS | COMPARISON WITH RANDOM SEARCH | |
| Wattanapongskorn06C | Fault-tolerant embedded system design and optimization considering Reliability estimation uncertainty | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Wiangtong02CL | Comparing Three Heuristics Search Methods for Functional partitioning in Hardware-Software Codesign | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | AREA, PENALTY, PROHIBIT |
PENALTY, PROHIBIT |
SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING, TABU SEARCH |
OTHER PROBLEM SPECIFIC | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Yang07EAB | Multi-objective Evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | RUN-TIME | COST, ENERGY, PERFORMANCE |
PATH LOSS, PHYSICAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | OTHER PROBLEM SPECIFIC | EXPERIMENTS | NOT PRESENTED | |
| Younis03AK | Optimization of Task Allocation in a Cluster–Based Sensor Network | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | TIMING, PHYSICAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ALLOCATION | EXPERIMENTS | INTERNAL COMPARISSON | |
| Zeng04BNDKC | QoS-Aware Middleware for Web Services Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY, REPUTATION |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC |
INTEGER PROGRAMMING ALGORITHM | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | NOT PRESENTED | |
| Zhang07SC | DiGA: Population diversity handling genetic algorithm for QoS-aware web services Selection | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING |
SERVICE SELECTION | EXPERIMENTS | INTERNAL COMPARISSON | |
| Zhang07YTF | QoS-driven Service Selection Optimization Model and Algorithms for Composite Web Services | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | GENERAL, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | DYNAMIC PROGRAMMING | SERVICE SELECTION | EXPERIMENTS | INTERNAL COMPARISSON | |
| Liang07C | Redundancy Allocation of series-parallel systems using a variable neighborhood search algorithm | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Tang10A | A Hybrid Genetic Algorithm for the Optimal Constrained Web | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | INTERNAL COMPARISSON | |
| Moreira07VB | Scheduling Multiple Independent Hard-Real-Time Jobs on a Heterogeneous Multiprocessor | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE | THROUGHPUT, PERFORMANCE, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SCHEDULING | SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Thiele02CGK | Design Space Exploration of Network Processor Architectures | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AREA, COST, PERFORMANCE |
PERFORMANCE, MEMORY, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, ALLOCATION, SCHEDULING |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Arafeh08DT | A multilevel partitioning approach for efficient Tasks Allocation in heterogeneous distributed systems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE | STRUCTURAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GRAPH PARTITIONING, HILL CLIMBING, HYBRID, TABU SEARCH |
ALLOCATION | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Ceriani10FLST | Multiprocessor Systems-on-Chip Synthesis using Multi-Objective Evolutionary Computation | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AREA, PERFORMANCE |
MEMORY, MAPPING, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING, TABU SEARCH |
SCHEDULING, COMPONENT SELECTION, ALLOCATION |
BENCHMARK PROBLEMS, INDUSTRIAL CASE STUDY |
COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Chen10SK | Processing element allocation and dynamic scheduling codesign for multi-function SoCs | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | DYNAMIC PROGRAMMING, GREEDY |
ALLOCATION, SCHEDULING |
EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Cooray10MRK | RESISTing Reliability Degradation through Proactive Reconfiguration | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | RELIABILITY, AVAILABILITY |
RELIABILITY, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | LINEAR MIXED INTEGER | EXACT | NOT PRESENTED | ALLOCATION | INDUSTRIAL CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
||
| Erst93HB | Hardware Software Co-Synthesis for Micro controllers | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE |
TIMING, REPAIR |
REPAIR | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION | SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Gupta93D | Hardware Software Co-Synthesis for Digital Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE |
PERFORMANCE, UTILIZATION, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | ALLOCATION, OTHER PROBLEM SPECIFIC |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Li11EEC | An Evolutionary Multiobjective Optimization Approach to Component-Based Software Architecture Design | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, COST, GENERAL |
GENERAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | ALLOCATION, HARDWARE SELECTION |
ACADEMIC CASE STUDY | INTERNAL COMPARISSON, , |
||
| Pezoa09H | Task ReAllocation for Maximal Reliability in Distributed Computing Systems with Uncertain Topologies and Non-Markovian Delays | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | RELIABILITY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | ALLOCATION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Poladian04SGS | Dynamic Configuration of Resource-Aware Services | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, GENERAL |
QOS VALUES, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | COMPONENT SELECTION, ALLOCATION, SOFTWARE PARAMETERS, HARDWARE PARAMETERS |
ACADEMIC CASE STUDY | COMPARISON WITH EXACT ALGORITHM, , NOT PRESENTED |
||
| Taboada06Cb | MOEA-DAP: A new Multiple Objective Evolutionary Algorithm for solving Design Allocation Problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | |||||
| Meedeniya12AG | Architecture-driven reliability optimization with uncertain model parameters | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | MEMORY, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ALLOCATION | EXPERIMENTS, ACADEMIC CASE STUDY |
NOT PRESENTED, , NOT PRESENTED |
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| Shin00CS | Power Optimization of Real-Time Embedded Systems on Variable Speed Processors | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR CONTINOUS | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SOFTWARE PARAMETERS, SCHEDULING |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Girault09ST | Reliability versus performance for critical applications | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
PERFORMANCE, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | SOFTWARE REPLICATION, SCHEDULING |
BENCHMARK PROBLEMS | NOT PRESENTED | |
| Emberson09 | Searching For Flexible Solutions To Task Allocation Problems | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | RELIABILITY, PERFORMANCE |
PERFORMANCE, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | HILL CLIMBING, SIMULATED ANNEALING |
ALLOCATION, SCHEDULING |
EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Islam07S | A Multi Variable Optimization Approach for the Design of Integrated Dependable Real-Time Embedded Systems | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | PERFORMANCE, RELIABILITY |
PERFORMANCE, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | CLUSTERING, ALLOCATION |
EXPERIMENTS | NOT PRESENTED | |
| Moser10M | The Automotive Deployment Problem: A Practical Application for Constrained Multiobjective Evolutionary Optimisation | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
MAPPING, MEMORY, GENERAL |
GENERAL | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | EXPERIMENTS | INTERNAL COMPARISSON | |
| Cortellessa06MP | Automated Selection of Software Components Based on Cost/Reliability Tradeoff | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | DELIVERY TIME, RELIABILITY, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER LINEAR PROGRAMMING, LINEAR PROGRAMMING |
COMPONENT SELECTION | NOT PRESENTED | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Cortellessa08CMP | Experimenting the Automated Selection of COTS Components Based on Cost and System Requirements | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | REQUIREMENTS, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER LINEAR PROGRAMMING | COMPONENT SELECTION | NOT PRESENTED | NOT PRESENTED, NOT NEEDED, NOT PRESENTED |
|
| Adomi06AABBCCCDF | The MAIS approach to web service design | GENERAL | GENERAL | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | SERVICE SELECTION | NOT PRESENTED | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Tahaee10J | A Polynomial Algorithm for Partitioning Problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AREA, COST, PERFORMANCE |
AREA, COST, PERFORMANCE, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, WITH GUARANTEE |
INTEGER LINEAR PROGRAMMING, PROBLEM SPECIFIC WITH GUARANTEE |
PARTITIONING | MATHEMATICAL PROOF | NOT PRESENTED | |
| Aleti09BGM | ArcheOpterix: An Extendable Tool for Architecture Optimization of AADL Models | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | MAPPING, MEMORY, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | EXPERIMENTS | NOT PRESENTED | |
| Islam06LS | Dependability Driven Integration of Mixed Criticality SW Components | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
GENERAL, REPAIR |
REPAIR | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Malek07 | A User-Centric Framework for Improving a Distributed Software System’s Deployment Architecture | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | ALLOCATION | NOT PRESENTED | NOT PRESENTED | |
| Mikic-Rakic05MM | Improving Availability in Large, Distributed Component-Based Systems Via ReDeployment | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY | PROHIBIT, MEMORY, MAPPING |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION | EXPERIMENTS | COMPARISON WITH EXACT ALGORITHM | |
| Nicholson98 | Selecting a Topology for Safety-Critical Real-Time Control Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
MAPPING, DEPENDABILITY, MEMORY, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, ALLOCATION |
INDUSTRIAL CASE STUDY, EXPERIMENTS |
COMPARISON WITH EXACT ALGORITHM | |
| Qiu99P | Dynamic Power Management Based on Continuous-Time Markov Decision Processes | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR CONTINOUS | EXACT | EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC |
LINEAR PROGRAMMING, GREEDY |
SOFTWARE PARAMETERS | INDUSTRIAL CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Seo07MM | An Energy Consumption Framework for Distributed Java-Based Software Systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | GENERAL | ENERGY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | LINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | ALLOCATION, COMPONENT SELECTION, SCHEDULING |
EXPERIMENTS | NOT PRESENTED | |
| Sharma08J | Deploying Software Components for Performance | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Simunic00BGD | Dynamic Power Management for Portable Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | LINEAR CONTINOUS | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SOFTWARE PARAMETERS | SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Suri10JHPIS | A software integration approach for designing and assessing dependable embedded systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | CLUSTERING, ALLOCATION |
EXPERIMENTS | NOT PRESENTED | |
| Hamza-Lup08ASI | Component Selection strategies based on system requirements’ dependencies on Component attributes | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | COMPONENT SELECTION | ACADEMIC CASE STUDY | NOT PRESENTED | |
| Serban09VP | A New Component Selection Algorithm Based on Metrics and Fuzzy Clustering Analysis | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | COST | REQUIREMENTS, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | COMPONENT SELECTION | ACADEMIC CASE STUDY | INTERNAL COMPARISSON | |
| Vescan08G | A Hybrid Evolutionary Multiobjective Approach for the Component Selection Problem | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | REQUIREMENTS | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, GREEDY, HYBRID |
COMPONENT SELECTION | SIMPLE EXAMPLE | NOT PRESENTED | ||
| Vescan08Thesis | Construction Approaches for Component-Based Systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | REQUIREMENTS, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, METAHEURISTIC |
BRANCH AND BOUND, EVOLUTIONARY ALGORITHM, GREEDY |
COMPONENT SELECTION | ACADEMIC CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Vescan09 | A Metrics-based Evolutionary Approach for the Component Selection Problem | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | COST | REQUIREMENTS, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Dhakal08PH | Maximizing Service Reliability in Distributed Computing Systems with Random Failures: Theory and Implementation | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | RELIABILITY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | ALLOCATION | BENCHMARK PROBLEMS | COMPARISON WITH EXACT ALGORITHM, NOT PRESENTED, COMPARISSON WITH EXACT ALGORITHM |
|
| Simunic01BD | Energy-Efficient Design of Battery-Powered Embedded Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | OTHER PROBLEM SPECIFIC | INDUSTRIAL CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Elegbede01A | Availability Allocation to Repairable systems with genetic algorithms:a multi-objective formulation | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | AVAILABILITY, COST |
GENERAL, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR CONTINOUS | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
EXPERIMENTS | NOT PRESENTED | |
| Liang10L | Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY, COST, WEIGHT |
WEIGHT, VOLUME, PROHIBIT, REDUNDANCY LEVEL |
PROHIBIT | GENERAL, SIMPLE AGGREGATION FUNCTIONS |
NONLINEAR INTEGER, LINEAR INTEGER |
APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | COMPONENT SELECTION, HARDWARE REPLICATION |
SIMPLE EXAMPLE, BENCHMARK PROBLEMS |
COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Potena07 | Composition and Tradeoff of Non-Functional Attributes in Software Systems: Research Directions | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | RELIABILITY, DELIVERY TIME, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | COMPONENT SELECTION | NOT PRESENTED | NOT PRESENTED | |
| Edwards09GTPMSP | Architecture-Driven Self-Adaptation and Self-Management in Robotic Systems | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | , |
GENERAL | INDUSTRIAL CASE STUDY | , , |
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| Esfahani10KM | Taming Uncertainty in Self-Adaptation through Possibilistic Analysis | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | , |
SOFTWARE PARAMETERS, HARDWARE PARAMETERS |
, , |
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| Rezaie10NM | A Multi-Objective Particle Swarm Optimization for Web Service Composition | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | RUN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | APPROXIMATIVE | METAHEURISTIC | PARTICLE SWARM | SERVICE SELECTION | EXPERIMENTS | COMPARISON WITH BASELINE ALGORITHM | ||
| Wiesemann08HK | A Stochastic Programming Approach for QoS-Aware Service Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC |
MIXED-INTEGER LINEAR PROGRAMMING (MILP), STOCHASTIC PROGRAMMING |
SERVICE COMPOSITION | EXPERIMENTS | NOT PRESENTED | |
| Alighanbari06KH | Coordination and Control of Multiple UAVs with Timing and Loitering | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | TIMING, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC, METAHEURISTIC |
MIXED-INTEGER LINEAR PROGRAMMING (MILP), OTHER PROBLEM SPECIFIC, TABU SEARCH |
ALLOCATION | SIMPLE EXAMPLE | INTERNAL COMPARISSON | |
| Hashemi09G | Throughput-Driven Synthesis of Embedded Software for Pipelined Execution on Multicore Architectures | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, WITH GUARANTEE, PROBLEM-SPECIFIC HEURISTIC |
GRAPH PARTITIONING, GRAPH PARTITIONING WITH GUARANTEE, EXACT GRAPH PARTITIONING |
ALLOCATION | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Al-naeem05ARB | A Quality-Driven Systematic Approach for Architecting Distributed Software Applications | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | RELIABILITY, GENERAL |
COST, GENERAL, DELIVERY TIME |
GENERAL | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | GENERAL | ACADEMIC CASE STUDY | NOT PRESENTED, NOT NEEDED, NOT PRESENTED |
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| Malek12MM | An Extensible Framework for Improving a Distributed Software System’s Deployment Architecture | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL, PERFORMANCE, RELIABILITY, ENERGY |
MEMORY, PROHIBIT, MAPPING |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | APPROXIMATIVE | GENERAL | ALLOCATION | ACADEMIC CASE STUDY | NOT PRESENTED, , |
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| Chen06GS | Architecture-based Self-Adaptation in the Presence of Multiple Objectives | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | PROBLEM-SPECIFIC HEURISTIC | , , |
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| Ahmed10M | Concept-Based Partitioning for Large Multidomain Multifunctional Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | GENERAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | PARTITIONING | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Dai07L | Optimal Resource Allocation for Maximizing Performance and Reliability in Tree-Structured Grid Services | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | GENERAL | PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR CONTINOUS | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, HARDWARE REPLICATION |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Greiner03GW | Safety Systems Optimum Design by Multicriteria Evolutionary Algorithms | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, MAINTENANCE SCHEDULES, HARDWARE REPLICATION |
ACADEMIC CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Li09CWL | Fast Scalable Optimization to Configure Service Systems having Cost and Quality of Service Constraints | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE, COST |
PERFORMANCE, COST, MAPPING, PROHIBIT |
PROHIBIT | MODEL BASED | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | LINEAR PROGRAMMING, OTHER PROBLEM SPECIFIC |
ALLOCATION, HARDWARE REPLICATION |
EXPERIMENTS | NOT PRESENTED | ||
| Blickle97 | Theory of Evolutionary Algorithms and Application to System synthesis | EMBEDDED SYSTEMS | GENERAL | DESIGN-TIME | COST, PERFORMANCE |
GENERAL, PENALTY, REPAIR |
PENALTY, REPAIR |
SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, Scheduling |
EXPERIMENTS, INDUSTRIAL CASE STUDY |
NOT PRESENTED | |
| Skroch10 | Multi-criteria Service Selection with Optimal Stopping in Dynamic Service-Oriented Systems | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | RESTRICTED ENUMERATION OF ALL POSSIBLE SOLUTIONS, EXHAUSTIVE SEARCH |
SERVICE SELECTION | EXPERIMENTS | NOT PRESENTED | ||
| Lukasiewycz08GHT | Efficient Symbolic Multi–Objective Design Space Exploration | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | STRUCTURAL, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
INTEGER LINEAR PROGRAMMING | HARDWARE SELECTION, ALLOCATION |
INDUSTRIAL CASE STUDY, EXPERIMENTS |
COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Saxena10K | MDE-Based Approach for Generalizing Design Space Exploration | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | GENERAL | GENERAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | GENERAL | GENERAL | GENERAL | GENERAL | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Aneja04CN | Minimal-Cost System Reliability With Discrete-Choice Sets for Components | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | RELIABILITY, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | LINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | COMPONENT SELECTION | SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Aydin01MMM | Dynamic and Aggressive Scheduling Techniques for Power-Aware Real-time Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
OTHER EXACT PROBLEM SPECIFIC, OTHER PROBLEM SPECIFIC |
SOFTWARE PARAMETERS | SIMPLE EXAMPLE | NOT PRESENTED | |
| Coit06K | Multiple Weighted Objectives Heuristics for the Redundancy Allocation Problem | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Hassine06MI | A Constraint-Based Approach to Horizontal Web Service Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | GENERAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Hong99KQPS | Power Optimization of Variable Voltage Core-Base systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | LINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | HARDWARE SELECTION, HARDWARE PARAMETERS |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Mabrouk09BKGI | QoS-Aware Service Composition in Dynamic Service Oriented Environments | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
QOS VALUES, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT PRESENTED | |
| Manoj09SM | A state-space search approach for optimizing reliability and cost of execution in distributed sensor networks | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | RELIABILITY, ENERGY |
PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
OTHER PROBLEM SPECIFIC, OTHER EXACT PROBLEM SPECIFIC |
ALLOCATION | SIMPLE EXAMPLE | INTERNAL COMPARISSON | |
| Billionnet08 | Redundancy Allocation for Series-Parallel Systems Using Integer Linear Programming | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | WITH GUARANTEE | APPROX INTEGER LINEAR PROGRAMMING WITH GUARANTEE | HARDWARE REPLICATION, SOFTWARE REPLICATION |
EXPERIMENTS | NOT PRESENTED | |
| Eames09NS | DesertFD: a finite-domain constraint based tool for design space exploration | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | GENERAL | GENERAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE, EXACT |
WITH GUARANTEE, EXACT PROBLEM-SPECIFIC |
BRANCH AND BOUND BASED WITH GUARANTEE, BRANCH AND BOUND |
GENERAL | ACADEMIC CASE STUDY | INTERNAL COMPARISSON | |
| Youness09HSTISWM | Optimization Method for Scheduling Length and the Number of Processors on Multiprocessor Systems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | ALLOCATION, SCHEDULING |
BENCHMARK PROBLEMS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Esfahani11KM | Taming Uncertainty in Self-Adaptive Software | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | GENERAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | SOFTWARE PARAMETERS | ACADEMIC CASE STUDY | INTERNAL COMPARISSON, NOT NEEDED, |
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| Amari10D | Redundancy Optimization Problem with Warm-Standby Redundancy | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, VOLUME, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | COMPONENT SELECTION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Andrews03B | Using statistically designed Experiments for safety system optimization | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | COST, AVAILABILITY, PENALTY |
PENALTY | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | RESTRICTED ENUMERATION OF ALL POSSIBLE SOLUTIONS | HARDWARE SELECTION, HARDWARE REPLICATION, MAINTENANCE SCHEDULES |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Andrews04B | A branching search approach to safety system design optimisation | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | DESIGN, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC, BRANCH AND BOUND BASED |
HARDWARE SELECTION, HARDWARE REPLICATION, MAINTENANCE SCHEDULES |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Banerjee04N | Efficient Search Space Exploration for HW-SW Partitioning | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | AREA, PROHIBIT |
PROHIBIT | MODEL BASED | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ALLOCATION | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Benazouz10MMU | A New Method for Minimizing Buffer Sizes for Cyclo-Static Dataflow Graphs | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PERFORMANCE, PRECEDENCE, REPAIR |
REPAIR | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | BRANCH AND BOUND BASED, GRAPH PARTITIONING, BRANCH AND BOUND |
SCHEDULING, SOFTWARE PARAMETERS |
INDUSTRIAL CASE STUDY | MATHEMATICAL PROOF | |
| Benini98HS | System-level Power Estimation And Optimization | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR CONTINOUS | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | SOFTWARE PARAMETERS | NOT PRESENTED | NOT PRESENTED | |
| Benini98MMPQ | Power Optimization of Core-Based Systems by Address Bus Encoding | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR CONTINOUS | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | CLUSTERING | NOT PRESENTED | NOT PRESENTED | |
| Blickle98TT | System-Level Synthesis Using Evolutionary Algorithms | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | COST, PERFORMANCE, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, ALLOCATION, SCHEDULING |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Boone10HSJJTDD | SALSA: QoS-aware load balancing for autonomous service brokering | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | PERFORMANCE | PERFORMANCE, PENALTY |
PENALTY | MODEL BASED | NONLINEAR CONTINOUS | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | PARTITIONING | EXPERIMENTS | NOT PRESENTED | |
| Castro10LB | Reducing Memory Requirements of Stream Programs by Graph Transformations | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER LINEAR PROGRAMMING | CLUSTERING, SCHEDULING |
BENCHMARK PROBLEMS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Trcka11HBGS | Integrated Model-Driven Design-Space Exploration for Embedded Systems | EMBEDDED SYSTEMS | GENERAL | DESIGN-TIME | GENERAL | GENERAL, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | GENERAL | GENERAL | GENERAL | INDUSTRIAL CASE STUDY | NOT PRESENTED, , |
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| Zheng03W | Heuristics Optimization of Scheduling and Allocation for Distributed Systems with Soft Deadlines | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | ALLOCATION, SCHEDULING |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Dave97LJ | COSYN: Hardware-Software Co-Synthesis of Embedded Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY, PERFORMANCE |
PERFORMANCE, REPAIR |
REPAIR | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | SCHEDULING, CLUSTERING |
INDUSTRIAL CASE STUDY, LITERATURE COMPARISON |
NOT PRESENTED | |
| Dave98J | COHRA: Hardware–Software Co-synthesis of Hierarchical Heterogeneous Distributed Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY, PERFORMANCE, RELIABILITY |
PERFORMANCE, REPAIR |
REPAIR | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | CLUSTERING, SCHEDULING |
INDUSTRIAL CASE STUDY, LITERATURE COMPARISON |
NOT PRESENTED | |
| Dick98J | MOGAC: A Multiobjective Genetic Algorithm for Hardware-Software Co-Synthesis of Distributed Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY |
COST, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, ALLOCATION, SCHEDULING |
EXPERIMENTS | NOT PRESENTED | |
| ElSayed01CW | Automation Support for Software Performance Engineering | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | TIMING, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | ALLOCATION | SIMPLE EXAMPLE | NOT PRESENTED | |
| Farnsworth10BTZ | A Novel Approach to Multi-level Evolutionary Design Optimization of a MEMS Device | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | OTHER PROBLEM SPECIFIC | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| FitzRoyDale09K | Towards automatic performance optimisation of componentised systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | EXHAUSTIVE SEARCH | COMPONENT SELECTION | ACADEMIC CASE STUDY | NOT PRESENTED | |
| Galvan07WGSM | New Evolutionary Methodologies for Integrated Safety System Design and Maintenance Optimization | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION, MAINTENANCE SCHEDULES, HARDWARE REPLICATION, SOFTWARE REPLICATION |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Glass10LHT | Lifetime Reliability Optimization for Embedded Systems: A System-Level Approach | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME, RUN-TIME |
GENERAL | MAPPING, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, HARDWARE REPLICATION |
INDUSTRIAL CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Gokhale04a | Cost Constrained Reliability Maximization of Software Systems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Gokhale04b | Software Application Design Based On Architecture, Reliability and Cost | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | INDUSTRIAL CASE STUDY | COMPARISON WITH EXACT ALGORITHM | |
| Grunske06 | Identifying Good Architectural Design Alternatives with MultiObjective Optimisation Strategies | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
WEIGHT, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
NOT PRESENTED | NOT PRESENTED | |
| Henkel94EHB | Adaptation of Partitioning and High-Level Synthesis in Hardware/Software Co–Synthesis | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | AREA, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION | EXPERIMENTS | NOT PRESENTED | |
| Hou97S | Allocation of Periodic Task Modules with Precedence and Deadline Constraints in Distributed Real-Time Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | ALLOCATION, SCHEDULING |
EXPERIMENTS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Izosimov05PEP | Design Optimization of Time- and Cost-Constrained Fault-Tolerant Distributed Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY, COST |
TIMING, COST, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH, GREEDY |
SCHEDULING, ALLOCATION |
EXPERIMENTS | INTERNAL COMPARISSON | |
| Kastner02 | Synthesis Techniques and Optimizations for Reconfigurable Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AREA, PERFORMANCE |
PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | ALLOCATION, CLUSTERING, OTHER PROBLEM SPECIFIC |
EXPERIMENTS | NOT PRESENTED | |
| Kim06K | HW/SW Partitioning Techniques for Multi-Mode Multi-Task Embedded Applications | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | GENERAL | COST | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | ALLOCATION, SCHEDULING |
SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Koziolek11R | Towards A Generic Quality Optimisation Framework for Component Based System Models | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, HARDWARE SELECTION, SOFTWARE SELECTION |
NOT PRESENTED | NOT PRESENTED | |
| LeBeux10BNBLP | Combining mapping and partitioning exploration for NoC-based embedded systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, OTHER PROBLEM SPECIFIC |
ACADEMIC CASE STUDY | NOT PRESENTED | ||
| Li09CE | SLA-driven Planning and Optimization of Enterprise Applications | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE PARAMETERS, SOFTWARE PARAMETERS |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Limbourg08K | Multi-objective optimization of Generalized Reliability design problems using feature Models—A concept for early design stages | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
GENERAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION, HARDWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Marseguerra04ZP | A multiobjective genetic algorithm approach to the optimization of the technical specifications of a nuclear safety system | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MAINTENANCE SCHEDULES | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Marseguerra05ZP | Multiobjective spare part Allocation by means of genetic algorithms and Monte Carlo simulation | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Marseguerra07ZP | Genetic Algorithms and Monte Carlo Simulation for the Optimization of System Design and Operation | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, COMPONENT SELECTION |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Martens10AKM | A Hybrid Approach for Multi-attribute QoS Optimisation in Component Based Software Systems | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION, ALLOCATION, HARDWARE SELECTION, HARDWARE PARAMETERS |
ACADEMIC CASE STUDY | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Martens10KBR | Automatically Improve Software Architecture Models for Performance, Reliability, and Cost Using Evolutionary Algorithms | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION, ALLOCATION, HARDWARE SELECTION, HARDWARE PARAMETERS |
ACADEMIC CASE STUDY | COMPARISON WITH RANDOM SEARCH | |
| Meedeniya10BAG | Architecture-Driven Reliability and Energy Optimization for Complex Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY, ENERGY |
REDUNDANCY LEVEL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Menasce07D | Utility-based QoS Brokering in Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | PERFORMANCE | QOS VALUES, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | EXACT | EXACT STANDARD | EXHAUSTIVE SEARCH | SERVICE SELECTION | ACADEMIC CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
|
| Menasce07RG | QoS management in service-oriented architectures | INFORMATION SYSTEMS | GENERAL | RUN-TIME | PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | SERVICE SELECTION | EXPERIMENTS | NOT PRESENTED | |
| Menasce08CD | A Heuristics Approach to Optimal Service Selection in Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
PERFORMANCE, COST, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | RESTRICTED ENUMERATION OF ALL POSSIBLE SOLUTIONS | SERVICE SELECTION | ACADEMIC CASE STUDY | NOT PRESENTED | |
| Nicholson97B | Emergence of an Architectural Topology for Safety-Critical Real-Time Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, PERFORMANCE |
GENERAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, COMPONENT SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION, ALLOCATION |
SIMPLE EXAMPLE | NOT PRESENTED | |
| Ortmeier04R | Safety Optimization: A combination of fault tree analysis and optimization techniques | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | COST, SAFETY |
DESIGN, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR MIXED INTEGER | GENERAL | GENERAL | GENERAL | SOFTWARE PARAMETERS, MAINTENANCE SCHEDULES |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Papadopoulos04G | Evolving car designs using model-based automated safety analysis and optimisation techniques | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION, OTHER PROBLEM SPECIFIC |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Pattison99A | Genetic Algorithms in Optimal Safety Design | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, COMPONENT SELECTION, MAINTENANCE SCHEDULES, SOFTWARE REPLICATION |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Qiu00WP | Dynamic Power Management of Complex Systems Using Generalized Stochastic Petri Nets | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | GENERAL, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR MIXED INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SOFTWARE PARAMETERS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
||
| Ren98D | Design of Reliable Systems Using Static & Dynamic Fault Trees | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, PHYSICAL, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, HARDWARE REPLICATION |
INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Riauke07B | An offshore safety system optimization using an SPEA2-based approach | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, HARDWARE SELECTION, MAINTENANCE SCHEDULES, HARDWARE PARAMETERS |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Shankaran06BSBLMD | A Framework for (Re)Deploying Components in Distributed Real-time and Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR INTEGER | GENERAL | GENERAL | GENERAL | ALLOCATION | NOT PRESENTED | NOT PRESENTED | |
| Torres-Echeverria08MT | Design optimization of a safety-instrumented system based on RAMS+ C addressing IEC 61508 requirements and diverse Redundancy | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED, SIMPLE AGGREGATION FUNCTIONS |
NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SOFTWARE REPLICATION, HARDWARE REPLICATION |
ACADEMIC CASE STUDY | NOT PRESENTED | |
| Wadekar99G | Exploring Cost and Reliability Tradeoffs in Architectural Alternatives using a Genetic Algorithm | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PENALTY |
PENALTY | MODEL BASED | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | INDUSTRIAL CASE STUDY | NOT PRESENTED | |
| Yeh10H | Solving reliability redundancy allocation problems using an artificial bee colony algorithm | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, WEIGHT, VOLUME, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | ARTIFICIAL BEE COLONY ALGORITHM | SOFTWARE REPLICATION | SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |
| Zhao04L | Redundancy optimization problems with uncertainty of combining randomness and fuzziness | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | COST, PROHIBIT |
PROHIBIT | MODEL BASED | NONLINEAR CONTINOUS | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED |
|
Results with comments
| PaperID | Title | Domain | Comments | Dimen. | Comments | Phase | Comments | Quality attribute | Comments | Constraints | Comments | Constraint Handling | Comments | Quality Evalution | Comments | Optimisation problem class | Comments | Optimisation strategy I | Comments | Optimization Strategy II | Comments | Optimisation strategy III | Comments | Transformation operators | Comments | Approach Validation | Comments | Optimization validation | Comments | |
| Abdelzaher95S | Optimal Combined Task and Message Scheduling in Distributed Real-Time Systems | GENERAL | Hard real-time systems | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Task lateness;minimizing the maximal (over Tasks) lateness of the Tasks comparing to the deadlines (which should be non-positive) | PRECEDENCE, PHYSICAL, PROHIBIT |
Precedence = synchronization and mutual exclusion; physical = resource constraints, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
BRANCH AND BOUND, GREEDY |
2 versions | SCHEDULING | finds a schedule for Tasks and set deadlines for messages | EXPERIMENTS | generated Example | INTERNAL COMPARISSON | the two presented algorithms compared | |||||||
| Abraham08LZ | Particle Swarm Scheduling for Work-Flow Applications in Distributed Computing Environments | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time; | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | Variable Neighborhood Search (VNS) is a relatively recent metaHeuristics which relies on iteratively exploring neighborhoods of growing size to identify better local optima with shaking strategies. | ALLOCATION, SCHEDULING |
partitions Tasks to operations with Precedence constraints and schedule the Tasks on different machines, so the problem of Task Allocation is included as well | NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | compared with MSPSO (Multi-start PSO) and MSGA (Multi-start GA) | ||||||||||
| Agarwal10AS | Optimal redundancy allocation in complex systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY, PERFORMANCE |
Reliability, Performance; | GENERAL, PROHIBIT, COST, WEIGHT |
Cost, weight and power has been used. but the approach is generic., |
PROHIBIT | GENERAL | binary complex systems.; | NONLINEAR INTEGER | Redundancy allcation | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | SOFTWARE REPLICATION | SIMPLE EXAMPLE | NOT PRESENTED | |||||||||||
| Ardagna06GIMP | QoS-Driven Web Services Selection in Autonomic Grid Environments | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Simple Aditive Weighting | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
Execution Time, Reputation, Price , Availability, ; | TIMING, QOS VALUES, PROHIBIT |
Task Duration and Local Constraints as well as constraints on QoS values, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF;Simple AF(sum, product, max, average) | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | Multiple choice Multiple dimension Knapsack Problem (MMKP) | SERVICE SELECTION | NOT PRESENTED | Case Study Cost Allocation process | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Ardagna06P | Global and Local QoS Guarantee in Web Service Selection | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
Execution Time, Reputation, Price , Availability, ; | TIMING, QOS VALUES, PROHIBIT |
Task Duration and Local Constraints as well as constraints on QoS values, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF;Simple AF(sum, product, max, average) | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | Multiple choice Multiple dimension Knapsack Problem (MMKP) | SERVICE SELECTION | EXPERIMENTS | Experiments with generated Example | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Ardagna07P | Adaptive Service Composition in Flexible Processes | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, REPUTATION |
Reputation, Execution Time, Availability, Cost;Same as Zeng04BNDKC | STRUCTURAL, REQUIREMENTS, PROHIBIT |
Detailed set of considered constraints, Cplex that prohibts unfeasable solutions |
PROHIBIT | Cplex that prohibts unfeasable solutions | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (sum, product, min, max, average) with utility Functions | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | EXPERIMENTS | Simulation with generated Example | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Ardagna10M | Per-flow optimal service Selection for Web services based processes | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
Execution Time, Cost, Reputation; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | MB;Queuing networks | NONLINEAR INTEGER | EXACT | EXACT STANDARD | SEQUENTIAL QUADRATIC PROGRAMMING | "SNOPT uses an iterative Sequential Quadratic Programming (SQP) algorithm (Gill et al., 2002) where an augmented Lagrangian function is reduced along each search direction to ensure convergence." | SERVICE SELECTION | EXPERIMENTS | Experiments with generated Example | NOT NEEDED, COMPARISON WITH BASELINE HEURISTIC ALGORITHM |
Comparison with Sequential Quadratic Programming | ||||||||||
| Azaron09PKKS | Multi-objective Reliability optimization for dissimilar-unit cold-standby systems using a genetic algorithm | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
Reliability, Cost;4 objectives: minimize (purchase) Cost of Components, maximize system MTTF (mean time to failure), minimize system VTTF (variance of time to failure), maximize mission time Reliability | GENERAL, PROHIBIT |
no constraints, but goals for each objective, and a General fitness Function that measures the under-attainment of each goal, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Mathematically complex; involves graph theory & Markov processes | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | double strings using continuous relaxation based on reference solution updating | COMPONENT SELECTION | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | results are compared against the results of a discrete-time approximation technique; goal of validation = efficiency | ||||||||
| Balasubramanian10GDWLGS | A Model-driven QoS Provisioning Engine for Cyber Physical Systems | GENERAL | Cyber physical systems, that interacts in both domains | GENERAL | Optimization not presented | RUN-TIME | Real time QoS support | GENERAL | General;Quality attributes has not explicitly presented | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | SAF;Not Presented clearly, but seems to be SAFs. | NONLINEAR MIXED INTEGER | Paramter changes and configuration | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | ALLOCATION | INDUSTRIAL CASE STUDY | Case Study on a Cyber physical system | NOT PRESENTED | ||||||||
| Berbner06SRHS | Heuristics for QoS-aware Web Service Composition | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, PERFORMANCE |
Availability, Response Time, and Throughput;Similar to Zeng04BNDKC | NOT PRESENTED, NOT PRESENTED |
, First, the LP relaxation of the MIP formulation of the composition problem is solved using a standard algorithm (e.g. simplex). |
NOT PRESENTED | First, the LP relaxation of the MIP formulation of the composition problem is solved using a standard algorithm (e.g. simplex). | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product, Min) | LINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC, METAHEURISTIC |
OTHER PROBLEM SPECIFIC, SIMULATED ANNEALING, MIXED-INTEGER LINEAR PROGRAMMING (MILP) |
MIXED INTEGER PROGRAMMING + BACKTRACKING, SIMULATED ANNEALING OR RANDOM SWAPPING OF SERVICES (MUTATION)), Mixed Integer Programming + Backtracking, (2) Simulated Annealing or Random Swapping of Services (Mutation)) |
OTHER PROBLEM SPECIFIC | Service orchestration | EXPERIMENTS | Simulation with generated Example | NOT PRESENTED | comparison Mixed Integer Programming + Backtracking, vs. Simulated Annealing vs. Random Swapping of Services (Mutation)) | |||||||
| Bhunia10SR | Reliability stochastic optimization for a series system with interval component reliability via genetic algorithm | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | GENERAL, PENALTY |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | SAF; | NONLINEAR INTEGER | Redundancy allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NOT PRESENTED | SIMPLE EXAMPLE | NOT PRESENTED | ||||||||||||
| Burmester08GMOKS | Tool support for the design of self-optimizing mechatronic multi-agent systems | EMBEDDED SYSTEMS | Safety Critical System | GENERAL | No optimisation, but good foundation for runtime adaption | RUN-TIME | GENERAL | Not Presented; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | Not Presented; | NOT APPLICABLE | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | COMPONENT SELECTION | Architecture Transformation | SIMPLE EXAMPLE | Shutle system | NOT PRESENTED | |||||||||
| Busacca01MZ | Multiobjective optimization by genetic algorithms: application to safety systems | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | COST, RELIABILITY |
Net Profit, Reliability; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA | HARDWARE REPLICATION, HARDWARE SELECTION |
"the choice of the redundancy allocation for each node", "the types of components to be used" in a plant design setting |
ACADEMIC CASE STUDY | Protection System for a Radioactive Waste Strorage Tank | NOT PRESENTED | ||||||||
| Canfora05DEV | An Approach for QoS-aware Service Composition based on Genetic Algorithms | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
Execution Time, Availability, Reliability, Cost; | GENERAL, PENALTY |
Distance from constraint satisfaction as a Penalty Function, Static and Dymanic Penalty Functions are used |
PENALTY | Static and Dymanic Penalty Functions are used | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | EXPERIMENTS | Experiments with generated Case Study | NOT PRESENTED | comparison of the genetic algorithm with Integer programming | ||||||||
| Canfora06DEPV | Service Composition (re)Binding Driven by Application–Specific QoS | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
Cost, Colour Depth, Resolution;Used in the Case Study | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | two–points crossover and a mutation operator | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | ACADEMIC CASE STUDY | Case Study Academic -image manipulation process | NOT PRESENTED | ||||||||||
| Canfora08PEV | A Framework for QoS-Aware Binding and Re-Binding of CompositeWeb Services | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
Time, Reliability, Availability, Price; | GENERAL, PENALTY |
distance from constraint satisfaction, |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, max, average) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE COMPOSITION | ACADEMIC CASE STUDY | Travel Planerm, Journey Planer, Imange Processing Case Study | NOT PRESENTED | |||||||||||
| Cao05CL | Genetic Algorithm Utilized in Cost-Reduction Driven Web Service Selection | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | COST | Cost; | COST, NOT PRESENTED |
exclusion, |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION | ACADEMIC CASE STUDY | Travel Planer Case Study | NOT PRESENTED | |||||||||||
| Cardellini06CGM | A Framework for Optimal Service Selection in Broker-based Architectures with Multiple QoS Classes | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
Execution Time, Reputation, Cost; | STABILITY, FUNCTIONAL CORRECTNESS, QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | SEQUENTIAL QUADRATIC PROGRAMMING | SERVICE SELECTION | ACADEMIC CASE STUDY | Abstract Case Study (4+7 services) | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Cardellini07CGL | Flow-Based Service Selection forWeb Service Composition Supporting Multiple QoS Classes | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
Response Time, Availability, Cost; | QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | ACADEMIC CASE STUDY | Travel Planer Case Study | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Cardellini09CGPM | QoS-driven Runtime Adaptation of Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
Response Time, Availability, Cost; | QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | Not much details about the implementation is given here | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | EXPERIMENTS | Experiments with generated Example | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Chang09CL | An ant algorithm for balanced job scheduling in grids | GENERAL | Grid computing environment | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE | Completion time;via load balancing | NOT PRESENTED, NOT PRESENTED |
, does Not apply |
NOT PRESENTED | does Not apply | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | Adapted for the dynamic scheduling case | ALLOCATION, SCHEDULING |
(Task Allocation &) scheduling, Tasks assigned to processors dynamically, so including Precedence (scheduling) | INDUSTRIAL CASE STUDY | Applied to a grid of 25 computing nodes from the Taiwan UniGrid, but still on a very high level (so quite simple application) | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | namely, the proposed BACO (Balanced ACO) algorithm is compared with iACO (Improved ACO) [13], FPLTF (Fastest Processor to Largest Task First) [14], dynamic FPLTF [15], Sufferage [15], and random selection method in the experiments | ||||||
| Chou95OB | Interface Co-Synthesis Techniques for Embedded Systems | EMBEDDED SYSTEMS | More focused on HW level aspects of synthesis | GENERAL | DESIGN-TIME | COST | Cost;a method to achieve low Cost design | PERFORMANCE, PROHIBIT |
Not clearly presented, Not clear, but seems to be Prohibited |
PROHIBIT | Not clear, but seems to be Prohibited | SIMPLE AGGREGATION FUNCTIONS | SAF;Additve Functions for Cost objective | NONLINEAR INTEGER | design decisions are discrete. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Present an algorithm to guide the synthesize. | OTHER PROBLEM SPECIFIC | design decisions like interface, port Allocation, port width etc. | INDUSTRIAL CASE STUDY | video wrist watch Case Study | NOT PRESENTED | ||||||
| Coelho07 | An efficient particle swarm approach for Mixed-Integer programming in Reliability–Redundancy optimization applications | GENERAL | Redundancy Allocation problem in General | SINGLE-OBJECTIVE OPTIMIZATION | Reliability optimization | DESIGN-TIME | RELIABILITY | Reliability;Reliability optimization | VOLUME, WEIGHT, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability is computed as a formula, including the bridge Case | NONLINEAR INTEGER | as I understood, the variables in the optjmization are mix of Integer and non-int. So MIP is used. | APPROXIMATIVE | METAHEURISTIC | PARTICLE SWARM | Particle Swam optimization | HARDWARE REPLICATION, SOFTWARE REPLICATION |
RAP considering components in general. Applicable to both Software/Hardware | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compare the Performance on benchmark problems described in the literature | |||||||
| Coelho08 | Reliability–Redundancy optimization by means of a chaotic differential evolution approach | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, WEIGHT, VOLUME, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;A non Linear formula is used to compute Reliability, | NONLINEAR INTEGER | only Integer values can take to the variables. | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Differnential evolution with gentic algorithm | HARDWARE REPLICATION, SOFTWARE REPLICATION |
RAP considering components in general. Applicable to both Software/Hardware | INDUSTRIAL CASE STUDY | Case Study of gas turbine optimization | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compare the results with different implementations of differntial evolutiona algos. | ||||||||
| Coit00L | SYSTEM Reliability OPTIMIZATION WITH k-out-of-n SUBSYSTEMS | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;mission time Reliability (probability that the system survives a given mission time) | COST, WEIGHT, PENALTY |
Weight = sum of Component Weights; Cost = sum of Component Cost, |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | solution through reformulation as a zero-one Integer programming problem | HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
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| Coit01 | Cold-standby Redundancy optimization for nonRepairable systems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;mission time Reliability (probability that the system survives a given mission time) | WEIGHT, PROHIBIT, COST |
Cost = sum of componnent Cost + switching Cost for subsystems with Redundancy; Weight = sum of Component Weights + switching Weight for subsystems with Redundancy (authors claim that problem formulation can be easily extended to include further constraints), , Cost = sum of componnent Cost + switching Cost for subsystems with Redundancy; Weight = sum of Component Weights + switching Weight for subsystems with Redundancy (authors claim that problem formulation can be easily extended to include further constraints) |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR MIXED INTEGER | Switiching times and Redundancy level | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | authors also give hints to other solution methods including genetic algorithms | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Coit01J | MULTI-CRITERIA OPTIMIZATION: MAXIMIZATION OF A SYSTEM Reliability ESTIMATE AND MINIMIZATION OF THE ESTIMATE VARIANCE | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | the authors call it MOO, though it only deals with system Reliability and ist variance | DESIGN-TIME | RELIABILITY | Reliability, ReliabilityVariance;maximize mission time Reliability, minimize variance of mission time Reliability | GENERAL, NOT PRESENTED |
General problem specification with arbitrary constraint Functions, |
NOT PRESENTED | GENERAL | General;diverse approaches are discussed | NONLINEAR INTEGER | Redundancy Allocation | GENERAL | GENERAL | GENERAL | diverse approaches discussed | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | partial description of a Example; application to software engineering Not considered | NOT PRESENTED | ||||||
| Coit02S | Genetic algorithm to maximize a lower-bound for system time-to-failure with uncertain Component Weibull Parameters | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;maximize the Time To Failure (TTF) Percentile - which is the time t for which the mission time Reliability (probability of the system to survive until t) is greater than a given p (percentile) | GENERAL, PENALTY |
General problem specification with arbitrary (but Linear) constraint Functions: constraint C = sum of Component C's, |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability evaluation Function, which is Not a sum | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||||
| Coit03 | Maximization of system Reliability with a choice of Redundancy strategies | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;maximize mission time Reliability | COST, WEIGHT, PROHIBIT |
Weight = sum of Component Weights; Cost = sum of Component Cost, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability evaluation Function, which is Not a sum | NONLINEAR INTEGER | Redundancy Allocation | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices; additional discussion about active vs. cold-standby Redundancy | SIMPLE EXAMPLE | application to software engineering Not considered | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
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| Coit04JW | System Optimization With Component Reliability Estimation Uncertainty: A Multi-Criteria Approach | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | the authors call it MOO, though it only deals with system Reliability and ist variance | DESIGN-TIME | RELIABILITY | Reliability, ReliabilityVariance;maximize mission time Reliability, minimize variance of mission time Reliability | GENERAL, PROHIBIT |
General problem specification with arbitrary constraint Functions, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability evaluation Function, which is Not a sum | NONLINEAR INTEGER | Redundancy Allocation | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | non-Linear Integer programming | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
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| Coit96Sa | SOLVING THE Redundancy Allocation PROBLEM USING A COMBINED NEURAL NETWORK / GENETIC ALGORITHM APPROACH | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;minize system Cost | RELIABILITY, PROHIBIT |
mission time Reliability, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability evaluation with sub systems. Function Not clearly presented | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | with effective candidate fitness evaluation through neural network approximation | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | |||||||
| Coit96Sb | Reliability Optimization of Series-Parallel Systems using a Genetic Algorithm | EMBEDDED SYSTEMS | Not clear | SINGLE-OBJECTIVE OPTIMIZATION | Only Reliability is considered as an objective | DESIGN-TIME | RELIABILITY | Reliability; | COST, WEIGHT, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability evaluation with sub systems. Function Not clearly presented | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | with effective candidate fitness evaluation through neural network approximation | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | |||||||
| Coit98S | Redundancy Allocation to Maximize a Lower Percentile of the System Time-to-Failure Distribution | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;maximize given percentile of TTF (Time To Failure) distribution | GENERAL, PROHIBIT |
General problem specification with arbitrary constraint Functions, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||||
| Cortellessa09P | How Can Optimization Models Support the Maintenance of Component-Based Software? | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | Maintenance time (so DT?) | COST | Cost;Cost for maintenance (testing or new components) | DELIVERY TIME, RELIABILITY, PROHIBIT |
see Cortellessa06MP, encoded in Linear programming problem, so Not Presented of the above |
PROHIBIT | encoded in Linear programming problem, so Not Presented of the above | SIMPLE AGGREGATION FUNCTIONS | AF; | LINEAR INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | Lingo Solver, for old part, no optimisation presented for new scenario of a monitored system | COMPONENT SELECTION, OTHER PROBLEM SPECIFIC |
Two separate cases (first is new compared to Cortellessa06MP): If system is monitored, add testing effort for the faulty component to increase realiability. Otherwise, try to exchange some component to make whole system more reliable. | NOT PRESENTED | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
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| Salazar07R | Solving advanced multi-objective robust designs by means of Multiple objective Evolutionary algorithms (MOEA): A Reliability application | EMBEDDED SYSTEMS | Reliability focus | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | COST, RELIABILITY |
Cost, Reliaiblity; | COST, PROHIBIT |
Selcetion promotes feasibility over optimality., |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA II | HARDWARE REPLICATION, HARDWARE PARAMETERS |
p. 1, the reliability of a component p.1 |
ACADEMIC CASE STUDY | life-support system in a space capsule | NOT PRESENTED | |||||||
| Dipenta06EVCCD | WS Binder: a Framework to enable Dynamic Binding of Composite Web Services | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | Abstract;Response Time and Price is used in the Case Study | GENERAL, PENALTY |
Distance from constraint satisfaction as a Penalty Function, |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | OTHER PROBLEM SPECIFIC | Service orchestration | ACADEMIC CASE STUDY | Travel Planer Case Study | NOT PRESENTED | ||||||||||
| Dogan01O | Biobjective Scheduling Algorithms for Execution Time-Reliability Trade-off in Heterogeneous Computing Systems | GENERAL | Heterogeneous-computing systems | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
Completion time, failure probability;trade-off of the two | PRECEDENCE, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, GREEDY |
Both designed and compared in the paper | SCHEDULING | NOT PRESENTED | generated examples to evaluate the performance | INTERNAL COMPARISSON | ||||||||||
| Dong06Y | Optimizing Web Service Composition Based on QoS Negotiation | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
Response Time, Availability, Cost; | QOS VALUES, NOT PRESENTED |
This is really unclear, |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Very unclear | NOT APPLICABLE | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | ||||||||||
| Dubey10M | Utility-based Optimal Service Selection for Business Processes in Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE |
Response Time, Reliability, Availability, Cost; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, max, average) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | HILL CLIMBING | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | Experiments with generated Example | COMPARISON WITH EXACT ALGORITHM | Comparisson with exact | |||||||||||
| ElHaddad10MR | TQoS: Transactional and QoS-aware Selection algorithm for automatic Web service composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, AVAILABILITY, PERFORMANCE, REPUTATION |
; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Specially designed heuristic | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | |||||||||||
| Erbas05CP | Multiobjective Optimization and Evolutionary Algorithms for the Application Mapping Problem in Multiprocessor System-on-Chip Design | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY, PERFORMANCE |
Performance, Power, Cost; | STRUCTURAL, REPAIR |
Mapping of a communication link must be between the nodes to which the communicationg Tasks have been mapped. , Three different Repair strategies, also compared them in Experiments |
REPAIR | Three different Repair strategies, also compared them in Experiments | SIMPLE AGGREGATION FUNCTIONS | AF;the input data for processing times are retrieved by executing the code and getting the trace information? | NONLINEAR MIXED INTEGER | Nonlinear Mixed Integer problem (they say, see conclusion). Only constraints are the Nonlinear part, can even be Linearized. | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | non-Linear problem, is Linearised for comparison with Exact methods | ALLOCATION | mapping of Functionality to architecture (binding), Kahn processes (Tasks) and FIFO channels (communication) are mapped to resources. Repair strategies if communication choice does Not fit the Task binding. | INDUSTRIAL CASE STUDY | real world, encoder and decoder Case Study. Comparison with manually found solution for the encoder | INTERNAL COMPARISSON | quantitative Performance analysis of two state-of-the-art MOEAs examined in conjunction with an Exact approach with respect to Multiple criteria. quantitative comparison of operators and Repair strategies | ||||||
| Etminani07N | A Min-Min Max-Min Selective Algorihtm for Grid Task Scheduling | GENERAL | Grid computing environment | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE | Completion time; | NOT PRESENTED, NOT PRESENTED |
, does Not apply |
NOT PRESENTED | does Not apply | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | Combination of Max-Min and Min-Min heuristics for scheduling | SCHEDULING | NOT PRESENTED | generated examples to evaluate the performance | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | compared with the simple Max-Min and Min-Min heuristics | |||||||
| Falco07DST | Multiobjective Differential Evolution for Mapping in a Grid Environment | GENERAL | Grid computing environment | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
Completion time, resource utilization, reliability; | NOT PRESENTED, NOT PRESENTED |
, does Not apply |
NOT PRESENTED | does Not apply | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | SIMPLE EXAMPLE | four scenarios of different diffuculty | NOT PRESENTED | |||||||||
| Giese03BKST | Multi-Agent System Design for Safety-Critical Self-Optimizing Mechatronic Systems with UML | EMBEDDED SYSTEMS | Safety Critical System | GENERAL | No optimisation, but good foundation for runtime adaption | RUN-TIME | GENERAL | Not Presented; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | Not Presented; | NOT APPLICABLE | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | Architecture Transformation | SIMPLE EXAMPLE | Shutle system | NOT PRESENTED | |||||||||
| Giovanni10P | An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem | GENERAL | Manufacturing environment | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time; | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Not Presented explicitly, only implicit | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | enritched with a local search technique | ALLOCATION, SCHEDULING |
BENCHMARK PROBLEMS | A set of generated examples | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | compared with two other scheduling algorithms | |||||||||
| Guo07HLDLD | ANGEL: Optimal Configuration for High Available Service Composition | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | AVAILABILITY | Availability; | COST, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;based on the Redundancy level | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Self-invented (Maximum Availability First, Best Price/Availability First) | SERVICE COMPOSITION | REDUNDANCY ALLOCATION. Use different services for availability (backup services), so that is using both multiple software and hardware. | EXPERIMENTS | Experiments with generated Example | INTERNAL COMPARISSON | Comparision of the two invented guided search algorithms | |||||||||
| Hadj-Alouanee96BM | A Hybrid Genetic/Optimization Algorithm for a Task Allocation Problem | EMBEDDED SYSTEMS | Automotive industry | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Hardware Cost;The aim is to minimize the Cost of the processors and bandwith of communication in between via optimal Task Allocation. | PHYSICAL, PENALTY |
by the Physical we mean that each Task is assigned to Exactly one processor, Tasks do Not exceed processors' capacity, etc., The penalty added to the objective Function is the sum of Weighted squares of constraint violations |
PENALTY | The penalty added to the objective Function is the sum of Weighted squares of constraint violations | SIMPLE AGGREGATION FUNCTIONS | AF; | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION | Task Allocation (to ECUs) / Deployment? It resembles more "Deployment problem" while Tasks are treated as software units assigned to Hardware (without any order) | NOT PRESENTED | COMPARISON WITH EXACT ALGORITHM | in particular with a comercial 0-1 Integer programming software and a hybrid Allocation based on implicit enumeration | ||||||||
| He10GZ | Task Allocation and Optimization of Distributed Embedded Systems with Simulated Annealing and Geometric Programming | EMBEDDED SYSTEMS | Automotive and avionic systems | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Latency;average latency is being minimized | TIMING, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | combined with geometric programming (called in a subroutine) | ALLOCATION, SCHEDULING |
It presents an integrated optimization framework that jointly considers one or more of the following attributes: ask-toprocessor allocation, task priority assignment, task period assignment and bus access configuration. | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | ||||||||||
| Huang09QZ | Genetic-algorithm-based optimal apportionment of Reliability and Redundancy under Multiple objectives | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
Reliability, Cost;maximize mission time Reliability, minimize Cost (where Cost is the sum of Component Cost, and Component Cost takes into account mission time and Component Reliability) | VOLUME, WEIGHT, PENALTY |
Weight = sum of Component Weights + Weight of Components interconnection Hardware; Volume = a higher-level measure taking the number of Components exponentially into account, Penalty techniqe proposed by Deb |
PENALTY | Penalty techniqe proposed by Deb | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Component Selection and Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | niched pareto GA combined with constraint handling method | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
continuous set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||
| Huynh09M | Runtime Reconfiguration of Custom Instructions for Real-Time Embedded Systems | EMBEDDED SYSTEMS | real-time embedded systems | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY, PERFORMANCE |
processor utilization (Performance, energy);via minimizing processor utilization, the Performance and energy consumption are also minimized | TIMING, PROHIBIT |
time deadlines of custom instructions for scheduling, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Mathematically complex, defined recursivelly | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | DYNAMIC PROGRAMMING | algorithm of list scheduling | SCHEDULING | customization and runtime reconfiguration of processor instructions | SIMPLE EXAMPLE | simple | COMPARISON WITH EXACT ALGORITHM | Integer Linear programming | |||||||
| Jafarpour10K | QoS-aware Selection ofWeb Service Composition QoS-aware Selection ofWeb Service Composition Based on Harmony Search Algorithm | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY, REPUTATION |
Response Time, Reliability, Availability, Throughput, Price, Reputation; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | HARMONY SEARCH | This is implemented just as a random search | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | |||||||||||
| Kaya09U | Exact algorithms for a task assignment problem | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Performance;Minimize execution and communication cost (= time) | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple sum of times | NONLINEAR INTEGER | NP hard | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | based on A* search | ALLOCATION | They call it task assignment | EXPERIMENTS | on artificial graphs | NOT NEEDED, COMPARISON WITH BASELINE HEURISTIC ALGORITHM |
Compared to other heuristics: Independent Set approach and Unit Processor Distance approach | ||||||||
| Kishor07YK | Application of a Multi-objective Genetic Algorithm to solve Reliability Optimization Problem | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY |
Reliability, Cost;maximize mission time Reliability, minimize Cost | NOT PRESENTED, NOT PRESENTED |
although the authors propose upper / lower bounds for the objectives Reliability and Cost (seems Not feasible to me), Not clear, but seems to be Prohibited |
NOT PRESENTED | Not clear, but seems to be Prohibited | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Non Linear Functions for Cost and Reliability | NONLINEAR INTEGER | changing the Redundancy level | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA-II (non-dominated sorting genetic algorithm) | NOT PRESENTED | clear explanation missing; probably choice of Components of each subsystem out of a discrete set | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||
| Ko08KK | Quality-of-service oriented web service composition algorithm and planning architecture | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
Execution Cost, Execution time, Availability, Successful execution rate, Reputation, Frequency; | REDUNDANCY LEVEL, QOS VALUES, REPAIR |
, neighborhood search |
REPAIR | neighborhood search | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | Neighbor plan generation | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | ||||||||
| Kokash07D | Evaluating Quality of Web Services: A Risk-Driven Approach | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | RELIABILITY | Risk; | COST, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Risk Evaluation | NONLINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | SERVICE COMPOSITION | TRN:REDUNDANCY ALLOCATION | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | |||||||||||
| Kulturel-Konak02SC | Efficiently solving the Redundancy Allocation problem using tabu search | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;although the authors discuss Multiple problem types in the paper, also multi-objective ones | COST, WEIGHT, PENALTY |
Weight = sum of Component Weights; Cost = sum of Component Cost, Constraints are included in the fitness Function |
PENALTY | Constraints are included in the fitness Function | NON-LINEAR MATHEMATICAL FUNCTIONS | AF;Reliability evaluation is a simple Function, but not a SAF | LINEAR INTEGER | changing the Redundancy level | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | the authors develop an own efficient algorithm: TSRAP (Tabu Search for the Redundancy Allocation Problem) | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | goal of validation = efficiency and quality of results | |||||
| Kulturel-Konak07CB | Pruned Pareto-optimal sets for the system Redundancy Allocation problem based on Multiple prioritized objectives | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, WEIGHT |
Reliability, Cost, Weight;maximize mission time Reliability, minimize Cost, minimize Weights | NOT PRESENTED | Constraints are included in the fitness Function | NOT PRESENTED | Constraints are included in the fitness Function | NON-LINEAR MATHEMATICAL FUNCTIONS | AF;Reliability evaluation is a simple Function, but not a SAF | LINEAR INTEGER | changing the Redundancy level | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | the MTS (multinomial tabu search) algorithm is used | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||
| Kunzli05TZ | A Modular Design Space Exploration Framework for Embedded Systems | EMBEDDED SYSTEMS | General framework for optimisation Tasks in embedded system design, on different abstraction levels | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | Any blackbox Function;Any Function can be plugged into the framework | GENERAL, PENALTY |
Constraints can be added to the problem-dependent fitness assignment part, Any penalty can be aded to fitness. Also dominance relation can be adjusted |
PENALTY | Any penalty can be aded to fitness. Also dominance relation can be adjusted | GENERAL | any;Black box fitness Function, needs to be implemented in a problem specific way | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | ANY METAHEURISTICS | Any metaHeuristics (both population based and trajectory based) | GENERAL | problem-specific implementation of variation operators | SIMPLE EXAMPLE | NOT PRESENTED | Not applicable, as they include existing approaches that have been validated before. | |||||||
| Kunzli06 | Efficient Design Space Exploration for Embedded Systems | EMBEDDED SYSTEMS | General framework for optimisation Tasks in embedded system design, on different abstraction levels | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | Any blackbox Function;Any Function can be plugged into the framework | GENERAL, PENALTY |
Constraints can be added to the problem-dependent fitness assignment part, Any penalty can be aded to fitness. Also dominance relation can be adjusted |
PENALTY | Any penalty can be aded to fitness. Also dominance relation can be adjusted | GENERAL | any;They suggest a new hybrid Performance evaluation approach for ES. In General, any blackbox fitness Function | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | ANY METAHEURISTICS | Any metaHeuristics (both population based and trajectory based) | GENERAL | problem-specific implementation of variation operators | ACADEMIC CASE STUDY | packet processor application, looks as realistic as a real world example, but does not seem to be one | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | comparison with SPEA, NSGA-II, FEMO, SEMO | ||||||
| Laalaoui09DBA | Ant Colony System with Stagnation Avoidance For the Scheduling of Real-Time Tasks | GENERAL | Discussed for embedded systems | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | described as pre-run-time | PERFORMANCE | Completion time;namely the success ratio of tasks scheduled before their deadline | TIMING, PRECEDENCE, PROHIBIT |
PROHIBIT | NOT PRESENTED | Not Presented;Could be SAF, if needed in our paper | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | SCHEDULING | On a single-processor architecture | NOT PRESENTED | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | compared with the original algorithm that has been modified in the paper | |||||||||
| Lee10KH | A Systematic Design Space Exploration of MPSoC Based on Synchronous Data Flow Specification | EMBEDDED SYSTEMS | System on chip | SINGLE-OBJECTIVE OPTIMIZATION | Cost minimization | DESIGN-TIME | COST | Cost;system cost | PERFORMANCE, REPAIR |
Real-time requirements, Inner loop to check real time constraints, which is a Repair function |
REPAIR | Inner loop to check real time constraints, which is a Repair function | SIMPLE AGGREGATION FUNCTIONS | SAF;Simple additive cost function | LINEAR INTEGER | component selection and shedule | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | COMPONENT SELECTION, ALLOCATION, SCHEDULING |
INDUSTRIAL CASE STUDY, EXPERIMENTS |
Case Study on a DVR | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |||||||
| Liang07LC | Variable neighbourhood search for Redundancy Allocation problems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION, MULTI-OBJECTIVE OPTIMIZATION |
DESIGN-TIME | RELIABILITY, COST |
Reliability;maximize mission time Reliability, Reliability, Cost;maximize TTF distribution (using a self-defined operator for comparison of two distributions); minimze system Cost (which is sum of Component Cost) |
COST, WEIGHT, PENALTY |
Cost = Cost of subsystem Cost, Weight = sum of subsystem Weights, A specific penalty Function has been used |
PENALTY | A specific penalty Function has been used | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, VARIABLE NEIGHBOURHOOD SEARCH |
based on NSGA-II (non-dominated sorting genetic algorithm), |
HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | goal of validation = efficiency and quality of results | |||||
| Lukasiewycz10GT | Robust Design of Embedded Systems | EMBEDDED SYSTEMS | Minimise risk of Cost due to design revisions. Requires an extensive Model of what design decisions are made for each(!) candidate solution, by what probability it could change, and how Costly a change is. | MULTI-OBJECTIVE OPTIMIZATION | Any number of objectives plus robustness | DESIGN-TIME | GENERAL | Robustness, any number of other qualities;Robustness is defined as risked Cost, can also take degradations of iother qualities into account. Note that robustness depends on the other found candidates so far, so robustness of _all_ candidates in the population needs to be re-evaluated in each iteration. | NOT PRESENTED, NOT PRESENTED |
Not the focus, |
NOT PRESENTED | GENERAL | any;no further details | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | although approach is Not restricted to that, could be any iterative MOO technique | GENERAL | INDUSTRIAL CASE STUDY, EXPERIMENTS |
2 example Case Study, one larger synthetic numerical experiment | NOT PRESENTED | ||||||||
| Marseguerra06M | Basics of genetic algorithms optimization for RAMS applications | EMBEDDED SYSTEMS | Safety Critical System | GENERAL | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST, MAINTAINABILITY, RELIABILITY, SAFETY |
Reliability, Availability, maintainability and safety (RAMS), Cost; | DESIGN, PENALTY |
Penalty Function, |
PENALTY | NOT PRESENTED | Not Presented; | NOT APPLICABLE | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE PARAMETERS, HARDWARE REPLICATION, MAINTENANCE SCHEDULES |
the parameters related to the inherent equipment reliability (e.g. per demand failure probability, failure rate, etc.)p.978, the system logic configuration (e.g. number of redundant trains, etc.), p.978, those [parameters] relevant to testing and maintenance activities (test intervals, maintenance periodicities, renewal periods, maintenance effectiveness, mean repair times, allowed downtimes, etc.) p.978 |
INDUSTRIAL CASE STUDY | Reactor Protection System | NOT PRESENTED | ||||||||
| Martorell04SCS | Alternatives and challenges in optimizing industrial safety using genetic algorithms | EMBEDDED SYSTEMS | Safety Critical System | GENERAL | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST, MAINTAINABILITY, RELIABILITY, SAFETY |
Reliability, Availability, maintainability and safety (RAMS), Cost; | DESIGN, PENALTY |
Penalty Function, |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA, SPEA2 | MAINTENANCE SCHEDULES | "For example, the case of application will focus on parameters related with testing and maintenance activities" (p. 26), but actually a general approach: "The selection of the parameters that will act as decision variables being involved in the MCDM problem depends on which problem is going to be solved" (p. 26) | INDUSTRIAL CASE STUDY | simplified highpressure injection system (HPIS) of a pressurized water reactor (PWR) | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | SOO= steady-state genetic algorithm (SSGA), MOO =SPEA2 | ||||||
| Menasce10EGMS | A Framework for Utility-Based Service Oriented Design in SASSY | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, PERFORMANCE, SECURITY |
Execution Time, Availability, Throughput, Security;Use of Utility Functions | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | HILL CLIMBING | Neighbourhood Search (Local Search | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | ACADEMIC CASE STUDY | Case Study Academic -emergency response application | NOT PRESENTED | ||||||||||
| Naderi10GA | A high performing metaheuristic for job shop scheduling with sequence-dependent setup times | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time;called makespan | NOT PRESENTED, NOT PRESENTED |
there are Precedence constraints among operations of each jobs, but such an encoding of the problem is used that infeasible solutions are not expressable, does Not apply |
NOT PRESENTED | does Not apply | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Not Presented explicitly, only implicit | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | extended with novel operators | SCHEDULING | Multiple processors, but the processors used for executing each jobs are given | NOT PRESENTED | just the benchmark (generated experimantal examples) used for algorithm comparison | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | namely with genetic algorithm proposed by Cheung et al. (called GA), immune algorithm of Zhou et al. (called IA), the hybrid genetic algorithm of Naderi et al. (called HGA), variable neighborhood search of Roshanaei et al. (called VNS) and SPT of Sule. | |||||||
| Nicholson96P | Design Synthesis Using Adaptive Search Techniques and Multi-Criteria Decision Analysis | EMBEDDED SYSTEMS | Topology Selection of SC-RT systems | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | multi objective optimization with Weighted sums. | DESIGN-TIME | COST, RELIABILITY, SAFETY |
Cost, Reliability, topology size;Cost, Reliability Functions | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | Not Presented; | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, COMPONENT SELECTION, ALLOCATION |
topology configuration | NOT PRESENTED | NOT PRESENTED | |||||||||
| Oh99H | A Hardware-Software Cosynthesis Technique Based on Heterogeneous Multiprocessor Scheduling | EMBEDDED SYSTEMS | Approach like in the middle of System On Chip(SOC) and Distributed Heterogeneous Embedded(DHE) systems | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Multiple objectives are transformed to single overhead metric using Weighted sum | DESIGN-TIME | PERFORMANCE | overhead;Minimize overhead that satisfies the Performance constraints, objective Functions are Not clear | PERFORMANCE, PROHIBIT |
deadline achievement, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF; | LINEAR INTEGER | Scheduling and Deployment | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Greedy Heuristics for Cost minimization with BIL(best imaginary level) scheduling algorithm | ALLOCATION, SCHEDULING |
EXPERIMENTS | Present an example and conduct a series of Experiments as well | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compare with COSYN,MOGAC and HOU approaches | ||||||
| Ouzineb08NG | Tabu search for the Redundancy Allocation problem of homogenous series–parallel multi-state systems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;minimze system Cost (= sum of Component Cost) | AVAILABILITY, PENALTY |
multi-state stationary Availability (may be interpreted as the probability that the system can supply a given demand load), Penalty Weights |
PENALTY | Penalty Weights | SIMPLE AGGREGATION FUNCTIONS | AF, universal generating Function;UGF used for evaluation of Availability constraint | LINEAR INTEGER | Redundancy allcation | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH | the approach first divides the search space into a set of disjoint subsets, and then applies TS to each subset (--> effective TS) | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | goal of validation = efficiency and quality of results | |||||
| Ouzineb10NG | An efficient heuristic for reliability design optimization problems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability ; | COST, WEIGHT, PROHIBIT |
PROHIBIT | GENERAL | ; | NONLINEAR INTEGER | Redundancy allcation, Scheduling | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, TABU SEARCH |
COMPONENT SELECTION | Changing the number of elements or versionnumbers in components | SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |||||||||||
| Painton95C | Genetic Algorithms in Optimization of System Reliability | GENERAL | but running example in the paper is taken from embedded systems domain | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;maximize system Mean Time Between Failures (MTBF) | COST, PROHIBIT |
Cost = sum of Component Cost, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Reliability block diagram used for visualization | LINEAR INTEGER | Component Selection | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION |
discrete set of Component choices | SIMPLE EXAMPLE | one running example throughout the paper that deals with embedded systems | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | the genetic algorithm was found to perform better than hill-climbing | |||||||
| Pimentel06EP | A Systematic Approach to Exploring Embedded System Architectures at Multiple Abstraction Levels | EMBEDDED SYSTEMS | Early design stages. Avoid need to simulate by using abstract analytical Models first | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, ENERGY, PERFORMANCE |
Performance, Power, Cost; | STRUCTURAL, REPAIR |
Mapping of a communication link must be between the nodes to which the communicationg Tasks have been mapped. , constraints are encoded in genes and then Repaired ? |
REPAIR | constraints are encoded in genes and then Repaired ? | SIMPLE AGGREGATION FUNCTIONS | AF;the input data for processing times are retrieved by executing the code and getting the trace information? | NONLINEAR INTEGER | Mapping problem | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | non-Linear problem, is Linearised for comparison with Exact methods | ALLOCATION | Kahn processes (Tasks) and FIFO channels (communication) are mapped to resources. Repair strategies if communication choice does Not fit the Task binding. | INDUSTRIAL CASE STUDY | real world, encoder Case Study. | NOT PRESENTED | ||||||
| Pop09DC | Genetic Algorithm for DAG Scheduling in Grid Environments | GENERAL | Grid computing environment | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time; | TIMING, PRECEDENCE, PHYSICAL, PENALTY |
PENALTY | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Genetic algorithm | ALLOCATION, SCHEDULING |
NOT PRESENTED | demonstrated on an experimental cluster with a number of simple generated scenarios | NOT PRESENTED | ||||||||||
| Qin05J | A dynamic and reliability-driven scheduling algorithm for parallel real-time jobs executing on heterogeneous clusters | GENERAL | Parallel real-time jobs | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | RELIABILITY | Reliability Cost;i.e. product of a failure rate and execution time | TIMING, PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF; | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | Problem specific optimization method | ALLOCATION, SCHEDULING |
NOT PRESENTED | just the generated experimantal examples, used for algorithm comparison | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | namely with DASAP and DALAP | |||||||||
| Raiha08KM | Genetic Synthesis of Software Architecture | GENERAL | Synthesis architecture from given responsibility graph: Architectural patters, class division | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Multiple objectives with Weighted sum | DESIGN-TIME | MODIFIABILITY, PERFORMANCE |
Modifiability, Efficiency;Both are measured with software metrics. Efficiency is measured with e.g. how many depending responsibilities are together in a class and how many dispatcher calls are required | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;OO design metrics | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | standard | ARCHITECTURAL PATTERN | Application of architectural patterns as the mutation step. Mutation probability for different patterns varies with time (first Dispatchers are more likely, later Facades). Input Model is responsibility graph. Repair strategies if patterns are broken in crossover | ACADEMIC CASE STUDY, EXPERIMENTS |
intelligent home system, resembles real world example but is not (see Raiha's thesis) ran Experiments with different Weights, | NOT PRESENTED | ||||||||
| Raiha09KM | Scenario-Based Genetic Synthesis of Software Architecture | GENERAL | Extension of Räihä08KM: Refined fitness Function, use scenarios to assess modifiability | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Multiple objectives with Weighted sum | DESIGN-TIME | MODIFIABILITY, PERFORMANCE |
Modifiability, Efficiency;Both are measured with software metrics (updated metric for modifiability). Efficiency is measured with e.g. how many depending responsibilities are together in a class and how many dispatcher calls are required | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;OO design metrics | NONLINEAR INTEGER | the length of the genome varies.. Is that a different class? | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | standard, with 1 point crossover | ARCHITECTURAL PATTERN | Application of architectural patterns as the mutation step. Mutation probability for different patterns varies with time (first Dispatchers are more likely, later Facades). Input Model is responsibility graph. Repair strategies if patterns are broken in crossover | ACADEMIC CASE STUDY | intelligent home system, robotwar system | INTERNAL COMPARISSON | Evaluated old results and current results both with new fitness Function. Naturally, the old results were worse. | ||||||
| Raiha09MP | Using simulated annealing for producing software architectures | GENERAL | Extension of Räihä08KM, comparison with simulated annealing | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Multiple objectives with Weighted sum | DESIGN-TIME | MODIFIABILITY, PERFORMANCE |
Modifiability, Efficiency;Both are measured with software metrics. Efficiency is measured with e.g. how many depending responsibilities are together in a class and how many dispatcher calls are required | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;OO design metrics | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ARCHITECTURAL PATTERN | Application of architectural patterns as the neighbour relation. | ACADEMIC CASE STUDY | intelligent home system | INTERNAL COMPARISSON | Compared results from Räihä08KM with simulated annealing. | ||||||||
| Rosenberg10MLMBD | MetaHeuristics Optimization of Large-Scale QoS-Aware Service Compositions MetaHeuristics Optimization of Large-Scale | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | Abstract; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING |
SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | ||||||||||||
| Roshanaei09NJK | A variable neighborhood search for job shop scheduling with set-up times to minimize makespan | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time; | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | ALLOCATION, SCHEDULING |
NOT PRESENTED | Just the experimantal examples, used for algorithm comparison | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | namely with GA_Cheung, HGA_Naderi, IA_Cheung, SPT | |||||||||||
| Salazar06RG | Optimization of constrained Multiple-objective Reliability problems using Evolutionary algorithms | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | the problem is originally formulated as SO, but then transformed to MOO as a solution strategy | DESIGN-TIME | RELIABILITY | Reliability;maximize mission-time Reliability | COST, PENALTY |
Cost = sum of Component Cost, Penalty techniqe proposed by Deb |
PENALTY | Penalty techniqe proposed by Deb | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | problems with Integer options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA-II (non-dominated sorting genetic algorithm) | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | for 4 different problems / Example, the Performance of the NSGA-II algorithm is compared to the algorithm that was originally used to solve the problem | ||||
| Shan08W | Reliable design space and complete single-loop Reliability-based design optimization | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | General approach | DESIGN-TIME | But the concepts may be applicable to RT | GENERAL | General;Does Not specify, consider a non-Linear Function from the design to objective space | GENERAL, PROHIBIT |
Constraints are treated as Multiple probabilistic Functions, satisfaction critiera is achiving a threshold probability, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Assumes a Function, and use analytical methods | NONLINEAR CONTINOUS | variables are Not limited Integer | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | Analytical optimization | GENERAL | Not described specifically | MATHEMATICAL PROOF, SIMPLE EXAMPLE |
NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
Since the optimization is based on Mathematical proofs, the validty is clear?, Manually Added |
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| Sharma09A | Ant Colony Optimization Approach to Heterogeneous Redundancy in Multi-state Systems with Multi-state Components | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;mimize system Cost (= sum of Component Cost) | RELIABILITY, WEIGHT, PENALTY |
Reliability = probability that the system successfully serves all requests for a given mission time (involves Component reliabilities, Component capacities, and system workload); Weight evaluation Not Presented, UnReliability acts as penlty |
PENALTY | UnReliability acts as penlty | SIMPLE AGGREGATION FUNCTIONS | AF;Objective is a SAF, but the constraint is a NMF | LINEAR INTEGER | problems with Integer options | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | COMPONENT SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices, heterogeneous Redundancy allowed | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | |||||||
| Stuijk07BGC | Multiprocessor Resource Allocation for ThroughputConstrained Synchronous Data ow Graphs | EMBEDDED SYSTEMS | embedded multimedia systems | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | load ballancing; | THROUGHPUT, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | binary search? static-order scheduling | ALLOCATION, SCHEDULING |
strategy that binds Tasks from an application to the resources and schedules the Tasks and the inter-Task communication on the assigned resources | BENCHMARK PROBLEMS | a set of applications to compare the effectiveness of the method in different settings | NOT PRESENTED | |||||||||
| Taboada06BC | Practical solutions for multi-objective optimization: An application to system Reliability design problems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, WEIGHT |
Reliability, Cost, Weight;maximize mission time Reliability, minimize system Cost (= sum of Component Cost) and Weight (= sum of Component Weights) | NOT PRESENTED, NOT PRESENTED |
, Not clear, but seems to be Prohibited |
NOT PRESENTED | Not clear, but seems to be Prohibited | SIMPLE AGGREGATION FUNCTIONS | AF;Reliability evaluation Function is simple, multiplication | LINEAR INTEGER | problems with Integer options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA (non-dominated sorting genetic algorithm); the resulting Pareto set is pruned using two approaches (pseudo-ranking, Clustering through k-means algorithm) | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||
| Taboada06Ca | Data Clustering of Solutions for Multiple Objective System Reliability Optimization Problems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, WEIGHT |
Reliability, Cost, Weight;maximize mission time Reliability, minimize system Cost (= sum of Component Cost) and Weight (= sum of Component Weights) | NOT PRESENTED, NOT PRESENTED |
, Not clear, but seems to be Prohibited |
NOT PRESENTED | Not clear, but seems to be Prohibited | SIMPLE AGGREGATION FUNCTIONS | AF;Reliability evaluation Function is simple, multiplication | LINEAR INTEGER | problems with Integer options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA-II (non-dominated sorting genetic algorithm) | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
Component Selection, Redundancy Allocation | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||
| Taboada08EC | MOMS-GA: A Multi-Objective Multi-State Genetic Algorithm for System Reliability Optimization Design Problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY, COST, WEIGHT |
Availability, Cost, Weight; | NOT PRESENTED, NOT PRESENTED |
, Not clear, but seems to be Prohibited |
NOT PRESENTED | Not clear, but seems to be Prohibited | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;objectives are formulated as Functions, Availability is evaluated using universal z-Transformation | NONLINEAR INTEGER | Redundancy Allocation and Component Selection | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOMS-GA Multi objectice multi state genetic algorithm | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
Redundancy Allocation with non-identical Components | EXPERIMENTS | NOT PRESENTED | ||||||||
| Tian09LZ | A joint Reliability–Redundancy optimization approach for multi-state series–parallel systems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;minimize system Cost | AVAILABILITY, PROHIBIT |
multi-state Availability; evaluation and interpretation only roughly described, Not clear, but seems to be Prohibited |
PROHIBIT | Not clear, but seems to be Prohibited | SIMPLE AGGREGATION FUNCTIONS | AF, universal generating Function;UGF used for evaluation of Availability constraint | LINEAR INTEGER | the design variables can only take Integer values | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices, Component has Multiple Reliability states; state distribution influenced by choice of technical and organizational actions | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | |||||||
| Tindell92BW | Allocating Hard Real Time Tasks (An NP-Hard Problem Made Easy) | GENERAL | A very simple SW/HW architecture is used to illustrate the approach | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Weighted sum of the main objectives and the penalties | DESIGN-TIME | PERFORMANCE | bus utilization;In this paper the feasible Allocation with the lowest bus utilisation is preferred - since more soft real time messages could meet their deadlines with a lower bus utilisation | TIMING, PHYSICAL, PENALTY |
also schedulability helping to meet the Timing, |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | ALLOCATION, SCHEDULING |
addresses only "schedulability" to meet the timing deadlines in the optimal Allocation | SIMPLE EXAMPLE | COMPARISON WITH EXACT ALGORITHM | the Exact solution was found for a small problem instance with brute force, and other techniques to Generalize the result to larger problem instances was discussed | ||||||||
| Vanrompay08RB | Genetic Algorithm-Based Optimization of Service Composition and Deployment | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
Response Time, Reliability, Availability, Cost; | MEMORY, PROCESSING POWER, PENALTY |
distance from constraint satisfaction, |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE COMPOSITION | NOT PRESENTED | No Validation | NOT PRESENTED | |||||||||||
| Wada08CSO | Multiobjective Optimization of SLA-aware Service Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
Latency Throughput, Cost; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF (Sum, Product) | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA | SERVICE COMPOSITION | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | |||||||||||
| Wang03GK | A New Approach for Task Level Computational Resource Bipartitioning | EMBEDDED SYSTEMS | Hardware/software codesign | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time;can be any Performance metrics such as the Hardware Cost, power consumption and worst Case execution time | COST, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;the formula to compute the quality is actually Not given, it is just said that it is computed as the overall execution time | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ANT COLONY OPTIMIZATION | Manually Added | ALLOCATION | partitioning of Tasks to be allocated on different resources | BENCHMARK PROBLEMS | a set of testing Example | COMPARISON WITH RANDOM SEARCH | |||||||||
| Wattanapongskorn06C | Fault-tolerant embedded system design and optimization considering Reliability estimation uncertainty | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | the authors call it MOO, though it only deals with system Reliability and ist variance | DESIGN-TIME | RELIABILITY | Reliability, ReliabilityVariance;maximize mission time Reliability, minimize variance of mission time Reliability | COST, PENALTY |
Cost = sum of Component Cost, Dynamic penalty Function |
PENALTY | Dynamic penalty Function | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Reliability evaluation Function, which is Not a sum | LINEAR INTEGER | problems with Integer options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
as a special feature, the approach distinguishes between Hardware and software Components and offers discrete sets of choices for both of them | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | |||||||
| Wiangtong02CL | Comparing Three Heuristics Search Methods for Functional partitioning in Hardware-Software Codesign | EMBEDDED SYSTEMS | Comparison of metaHeuristics for Task partitioning problem (for sequential scheduling, resources are given). Goal: choose which Tasks are implemented in SW, which in HW | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Performance;processing time | AREA, PENALTY, PROHIBIT |
Only a given area is available, Penalty in Evolutionary alg., Prohibit in simulated annealing and tabu search(?) Whether Tabu Search (TS) really uses penalty is unclear, it is Not described, so I assume they do the same as for the previous section (SA) |
PENALTY, PROHIBIT |
Penalty in Evolutionary alg., Prohibit in simulated annealing and tabu search(?) Whether Tabu Search (TS) really uses penalty is unclear, it is Not described, so I assume they do the same as for the previous section (SA) | SIMPLE AGGREGATION FUNCTIONS | AF;first allocate software Tasks, then Hardware Tasks (sort first), then calculate fitness | NONLINEAR INTEGER | discuss that they canNot use Linear programming because of the intractability of the problem (considering resource conflicts). | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING, TABU SEARCH |
enhanced by some kind of local search: optimal and feasible Allocation for the choice of whether a Task is implemented in SW or HW | OTHER PROBLEM SPECIFIC | whether a Task is implemented in software or Hardware | EXPERIMENTS | artificially generated Task graphs | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | tabu search once as is and once with their proposed penalty Function | |||||
| Yang07EAB | Multi-objective Evolutionary optimizations of a space-based reconfigurable sensor network under hard constraints | EMBEDDED SYSTEMS | Satellite networks | MULTI-OBJECTIVE OPTIMIZATION | RUN-TIME | However, human needs to make trade-off decision, this is a bit contradicting | COST, ENERGY, PERFORMANCE |
energy consumption, battery lifetime, coverage, number of satellites;min energy, may lifetime, max coverage, min number of satellites | PATH LOSS, PHYSICAL, PENALTY |
bit-energy-to-interference-density-ratio, upper bouds for T and power consumption, The dominance relation considers the constraints. They do Not say this explicitly, but this is how it is explained in the referenced NSGA-II paper (Deb2002, IEEE Transac. On. EC) |
PENALTY | The dominance relation considers the constraints. They do Not say this explicitly, but this is how it is explained in the referenced NSGA-II paper (Deb2002, IEEE Transac. On. EC) | SIMPLE AGGREGATION FUNCTIONS | AF;Not so simple, though. | NONLINEAR MIXED INTEGER | explicitly said in paper (phew) | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA-II | OTHER PROBLEM SPECIFIC | transmission power, transmission time; a number of bits can be sent with a certain power and over a certain time | EXPERIMENTS | Some example runs of the algorithm, no description about the system (whether it is realistic), but it could be. Simulations | NOT PRESENTED | Not required as they just use the EMO out of the box | ||||
| Younis03AK | Optimization of Task Allocation in a Cluster–Based Sensor Network | EMBEDDED SYSTEMS | Sensor networks | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | life-time of the sensor network;maximize the lifetime by minimizing and balancing the energy consumed by clusters in the network | TIMING, PHYSICAL, PROHIBIT |
also schedulability helping to meet the Timing, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;the energy consumption is an additive Function, but when transformed to the network lifetime, it becomes more complicated | NONLINEAR INTEGER | 0-1 Nonlinear - in particular | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | an algorithm designed elsewhere is employed | ALLOCATION | Task Allocation (to sensor gateways), gateways to the clusters of sensors | EXPERIMENTS | Example with varying load of sensors | INTERNAL COMPARISSON | compared with non-optimized version of the algorithm | ||||||
| Zeng04BNDKC | QoS-Aware Middleware for Web Services Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY, REPUTATION |
Response Time, Reliability, Availability, Throughput, Price, Reputation; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC |
INTEGER PROGRAMMING ALGORITHM | Local & Global Planing with Integer Programming (Multi-objective with simple additive Weighting, positive & negative QoS Attributes) | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | For quality not needed for exact part | ||||||||||
| Zhang07SC | DiGA: Population diversity handling genetic algorithm for QoS-aware web services Selection | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | Abstract, Diversity; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | Not Presented; | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING |
SERVICE SELECTION | EXPERIMENTS | Experiments with generated Example | INTERNAL COMPARISSON | Comparisson of Coding Scemes etc. | |||||||||||
| Zhang07YTF | QoS-driven Service Selection Optimization Model and Algorithms for Composite Web Services | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | Abstract; | GENERAL, NOT PRESENTED |
NOT PRESENTED | NOT PRESENTED | Not Presented; | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | DYNAMIC PROGRAMMING | Dynamic Programing with construction of convex hull Heuristics | SERVICE SELECTION | EXPERIMENTS | Experiments with generated Example | INTERNAL COMPARISSON | Comparisson with and without the convex hull Heuristics | ||||||||||
| Liang07C | Redundancy Allocation of series-parallel systems using a variable neighborhood search algorithm | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;maximize mission time Reliability | COST, WEIGHT, PENALTY |
Cost = Cost of subsystem Cost, Weight = sum of subsystem Weights, A specific penalty Function has been used |
PENALTY | A specific penalty Function has been used | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | goal of validation = efficiency and quality of results,ant colony optimization, genetic algorithm, tabu searc | ||||||
| Tang10A | A Hybrid Genetic Algorithm for the Optimal Constrained Web | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | Abstract; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SERVICE SELECTION, SERVICE COMPOSITION |
EXPERIMENTS | Experiments with generated Example | INTERNAL COMPARISSON | Comparisson with different parameters | |||||||||||
| Moreira07VB | Scheduling Multiple Independent Hard-Real-Time Jobs on a Heterogeneous Multiprocessor | EMBEDDED SYSTEMS | real-time embedded systems | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | combination of run-time and static order scheduling | PERFORMANCE | processor utilization;if there are more processors (in a multi-processor Case), a Weighter sum is used | THROUGHPUT, PERFORMANCE, PROHIBIT |
, IM: Latency->Performance, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR INTEGER | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | combination of Time-Division Multiplex (TDM) and static-order scheduling | SCHEDULING | SIMPLE EXAMPLE | very simple | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Thiele02CGK | Design Space Exploration of Network Processor Architectures | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AREA, COST, PERFORMANCE |
chip area, on-chip Memory requirements, and Performance;Performance (such as the Throughput and the number of flow classes that can be supported) | PERFORMANCE, MEMORY, PROHIBIT |
in inner optmisation loop IM:Delay->Performance, in inner optmisation loop, constraints only used in inner, problem specific? Loop, Not in SPEA2 |
PROHIBIT | constraints only used in inner, problem specific? Loop, Not in SPEA2 | NON-LINEAR MATHEMATICAL FUNCTIONS | calculus;real-time calculus for reasoning about packet flows and their processing, Linear approximation to speed up the evaluation | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SPEA2, some inner optimisation of scheduler? | HARDWARE SELECTION, ALLOCATION, SCHEDULING |
ACADEMIC CASE STUDY | network processor | NOT PRESENTED | Not required as they just use the EMO out of the box | ||||||||
| Arafeh08DT | A multilevel partitioning approach for efficient Tasks Allocation in heterogeneous distributed systems | GENERAL | Example named are multiclusters and Grid environments, high Performance computing | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | initial scheduling, and bpossibly Dynamic load balancing | PERFORMANCE | Performance;application Completion time ( = computation time + communication latencies) | STRUCTURAL, PROHIBIT |
System constraints: limit on workload per processor, total time per processor must Not be less that application reservation period, Prohibitive in local search phase, |
PROHIBIT | Prohibitive in local search phase, | MODEL BASED | MB;Weighted, undirected graphs (system graph, Task interaction graph) | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GRAPH PARTITIONING, HILL CLIMBING, HYBRID, TABU SEARCH |
Very Heavy Edge Matching for Clustering (algorithmic?) | ALLOCATION | Mapping of Tasks to processors (Task Allocation -> Deployment) | EXPERIMENTS | generated example systems that are simulated, | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Their VHEM is compared to Heavy Edge Matching, a previously suggested Clustering algorithm for that problem. | |||||
| Ceriani10FLST | Multiprocessor Systems-on-Chip Synthesis using Multi-Objective Evolutionary Computation | EMBEDDED SYSTEMS | System on Chip (but applicable for Software Intensive systems in general) | MULTI-OBJECTIVE OPTIMIZATION | Preto dominance | DESIGN-TIME | AREA, PERFORMANCE |
Chip Area, Soft Deadlines, Hard Deadlines, size of local buffers;The approach can be extended to other quality attributes | MEMORY, MAPPING, PROHIBIT |
Does not use the same terminology, but the constraints are loc, and colloc, |
PROHIBIT | MODEL BASED | MB;Modes are used to evaluate deadline violations, area etc. but not specifically presented | NONLINEAR INTEGER | Mapping, Scheduling,structal reconfigurations etc. are in integer decision space | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, SIMULATED ANNEALING, TABU SEARCH |
Genetic Algorithm, Simmulated Annealing, Tabu Search. Use the evolutionary algorithms | SCHEDULING, COMPONENT SELECTION, ALLOCATION |
The paper discusses large number of various transformations. | BENCHMARK PROBLEMS, INDUSTRIAL CASE STUDY |
Case Study of JPEG compression, TGFF[4] generated benchmarks | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Reference solutions are genereated by running the algorithms for large number of iterations | |||||
| Chen10SK | Processing element allocation and dynamic scheduling codesign for multi-function SoCs | EMBEDDED SYSTEMS | System on Chip | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;Minimizing processing elements which contributes to the cost | PERFORMANCE, PROHIBIT |
Scheduling constraints, |
PROHIBIT | MODEL BASED | MB;STC evaluation model for timing evaluations | NONLINEAR INTEGER | Integer decision variables are altered. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | DYNAMIC PROGRAMMING, GREEDY |
Two algorithms are presented to cater specific conditions | ALLOCATION, SCHEDULING |
Processing element allocation, and task scheduling , Processing element allocation, and task scheduling |
EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Dynamic programming | |||||||
| Cooray10MRK | RESISTing Reliability Degradation through Proactive Reconfiguration | EMBEDDED SYSTEMS | Exact method. no need to compare with the optimal. | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | RELIABILITY, AVAILABILITY |
RELIABILITY, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | LINEAR MIXED INTEGER | EXACT | Select the most reliable solution from all possible solutions. | NOT PRESENTED | No optimisation is present. The focus is on obtaining a solution that satisfies requirements. | ALLOCATION | Task Allocation -> Deployment | INDUSTRIAL CASE STUDY | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
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| Erst93HB | Hardware Software Co-Synthesis for Micro controllers | EMBEDDED SYSTEMS | Microcontroller design | SINGLE-OBJECTIVE OPTIMIZATION | Minimize Cost satisfying the design goals/constraints | DESIGN-TIME | COST, PERFORMANCE |
Performance,Cost; | TIMING, REPAIR |
, when timing constraint violates, new hw is added. |
REPAIR | when timing constraint violates, new hw is added. | MODEL BASED | MB;Execution graphs for Performance Modeling | NONLINEAR INTEGER | Allocation problem. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Clustering Heuristics using closeness criteria | ALLOCATION | Allocating software Functions to Hardware | SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compare with benchmark problems | |||||
| Gupta93D | Hardware Software Co-Synthesis for Digital Systems | EMBEDDED SYSTEMS | Digital systems design | MULTI-OBJECTIVE OPTIMIZATION | Cost-Performance trade-offs in deciding whether to implement in HW or SW | DESIGN-TIME | COST, PERFORMANCE |
Performance,Cost;Partition Cost | PERFORMANCE, UTILIZATION, PROHIBIT |
min/max delay, Execution rate, Processor utilization, bus utilization, |
PROHIBIT | MODEL BASED | MB;Execution graphs for Performance Modeling, Partition Cost is considered as a Function of other Parameters. Has Not described clearly. | NONLINEAR INTEGER | Allocation problem. | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | Problem specific optimization method using execution graphs | ALLOCATION, OTHER PROBLEM SPECIFIC |
Deployment, implement in HW or SW decision | SIMPLE EXAMPLE | Many intuitive Example are included | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Li11EEC | An Evolutionary Multiobjective Optimization Approach to Component-Based Software Architecture Design | GENERAL | Component based software systems in general | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimality | DESIGN-TIME | PERFORMANCE, COST, GENERAL |
Processor utlilization, Dataflow latency, Architecture cost, has considered 3 QAs. But the approach is applicable in general |
GENERAL, PROHIBIT |
, Has not clearly mentioned. Seems prohibit |
PROHIBIT | Has not clearly mentioned. Seems prohibit | MODEL BASED | Construct evaluation models using ROBOCOP AADL | NONLINEAR MIXED INTEGER | APPROXIMATIVE | Evolutionary algorithms | METAHEURISTIC | Third party evolutionary algorithms | ALLOCATION, HARDWARE SELECTION |
Functionality distribution , hardware topology, and selection |
ACADEMIC CASE STUDY | Two case studies, Car Radio Navigation and Business Report System | INTERNAL COMPARISSON, , |
Compare the results of a set of evolutionary algorithms (NSGA-II, SPEA2, SMS-EMOA), |
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| Pezoa09H | Task ReAllocation for Maximal Reliability in Distributed Computing Systems with Uncertain Topologies and Non-Markovian Delays | GENERAL | distributed computing systems | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | RELIABILITY | Reliability;maximize the probability that all existing Tasks can be served before the system fails | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;The system of interconnected servers and the workload is Modeled | NONLINEAR INTEGER | Allocation problem. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | the authors describe a specific algorithm which solves the problem effectively but is Not guaranteed to find the optimal solution | ALLOCATION | Allocation of Tasks to the servers of the distributed computing system | SIMPLE EXAMPLE | one Example of a distributed computing system | NOT PRESENTED | ||||||||
| Poladian04SGS | Dynamic Configuration of Resource-Aware Services | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Overall utility is optimised | RUN-TIME | AVAILABILITY, GENERAL |
, QoS values is the option. but we have merged it |
QOS VALUES, NOT PRESENTED |
General resource constraints, |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | near optimal reconfiguration decisions | PROBLEM-SPECIFIC HEURISTIC | COMPONENT SELECTION, ALLOCATION, SOFTWARE PARAMETERS, HARDWARE PARAMETERS |
ACADEMIC CASE STUDY | COMPARISON WITH EXACT ALGORITHM, , NOT PRESENTED |
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| Taboada06Cb | MOEA-DAP: A new Multiple Objective Evolutionary Algorithm for solving Design Allocation Problems | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | NOT PRESENTED, NOT PRESENTED |
however, authors claim that solution could be Generalized to consider any number of constraints, Genetic operators are implemented only to generate feasible solutions |
NOT PRESENTED | Genetic operators are implemented only to generate feasible solutions | LINEAR INTEGER | problems with Integer options | APPROXIMATIVE | METAHEURISTIC | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
Component Selection, Redundancy Allocation | SIMPLE EXAMPLE | application to software engineering Not considered | |||||||||||||
| Meedeniya12AG | Architecture-driven reliability optimization with uncertain model parameters | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | MEMORY, PROHIBIT |
PROHIBIT | MODEL BASED | DTMCs | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | ALLOCATION | Deployment | EXPERIMENTS, ACADEMIC CASE STUDY |
NOT PRESENTED, , NOT PRESENTED |
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| Shin00CS | Power Optimization of Real-Time Embedded Systems on Variable Speed Processors | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | Energy;EC | PERFORMANCE, PROHIBIT |
Deadline achievement, Prohibit deadline vilation |
PROHIBIT | Prohibit deadline vilation | SIMPLE AGGREGATION FUNCTIONS | AF;Additive Models | LINEAR CONTINOUS | Adjust the CPU frequency which is in linear space | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | Algebraic solution | SOFTWARE PARAMETERS, SCHEDULING |
SIMPLE EXAMPLE | simulated Example | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Girault09ST | Reliability versus performance for critical applications | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Optimize one objective at a time, but combined as a two phase process | DESIGN-TIME | PERFORMANCE, RELIABILITY |
Execution time, reliability; | PERFORMANCE, PROHIBIT |
Deadline reachability constraints, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | Mathematical function to reliabililty, response time, which is not just an SAF; | NONLINEAR INTEGER | allocation, and scheduling deals with integer variables | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | SOFTWARE REPLICATION, SCHEDULING |
BENCHMARK PROBLEMS | Use benchmark problems from the literature | NOT PRESENTED | Use benchmark problems from the literature | |||||||
| Emberson09 | Searching For Flexible Solutions To Task Allocation Problems | EMBEDDED SYSTEMS | Avoinics and automotive domains | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Dependability, timing aspects are transformed to one sigle value | DESIGN-TIME | Have concerns on runtime, but Not specifically mentioned | RELIABILITY, PERFORMANCE |
Worst Case execution time and fault tolerance metrics; | PERFORMANCE, PENALTY |
WCET requirements, infeasible solutions are penalized |
PENALTY | infeasible solutions are penalized | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Allocation and ordering problem | APPROXIMATIVE | METAHEURISTIC | HILL CLIMBING, SIMULATED ANNEALING |
Two approaches are presented: Random restart hillclimbing and simulated annealing | ALLOCATION, SCHEDULING |
Task Allocation, scheduling | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | |||||
| Islam07S | A Multi Variable Optimization Approach for the Design of Integrated Dependable Real-Time Embedded Systems | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Simple Aditive Weighting | DESIGN-TIME | PERFORMANCE, RELIABILITY |
Interaction, scheduling length and bandwidth utilization; | PERFORMANCE, PROHIBIT |
Deadline constraints, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Failure propagation based interaction evaluation | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | CLUSTERING, ALLOCATION |
EXPERIMENTS | NOT PRESENTED | |||||||||||
| Moser10M | The Automotive Deployment Problem: A Practical Application for Constrained Multiobjective Evolutionary Optimisation | EMBEDDED SYSTEMS | Specific on automotive software Deployment problem | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
Data transmission Reliability and communication overhead; | MAPPING, MEMORY, GENERAL |
, compares Prohibit, panelty and Repair techniques |
GENERAL | compares Prohibit, panelty and Repair techniques | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Deployment | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA-II | ALLOCATION | EXPERIMENTS | INTERNAL COMPARISSON | different representations and constraint handling techniques has been compared using Experiments (30 runs each) | |||||||
| Cortellessa06MP | Automated Selection of Software Components Based on Cost/Reliability Tradeoff | GENERAL | no domain named | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;single Cost value for each option | DELIVERY TIME, RELIABILITY, PROHIBIT |
Delivery Time: development time including testing or procurement time, Reliability: POFOD, encoded in Linear programming problem, so Not Presented of the above |
PROHIBIT | encoded in Linear programming problem, so Not Presented of the above | SIMPLE AGGREGATION FUNCTIONS | AF; | LINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER LINEAR PROGRAMMING, LINEAR PROGRAMMING |
LINEAR OPTIMISATION (LINGO SOLVER), Linear optimisation (Lingo solver) |
COMPONENT SELECTION | Buy or develop in-house (with amount of testing as an additional Parameter) | NOT PRESENTED | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Cortellessa08CMP | Experimenting the Automated Selection of COTS Components Based on Cost and System Requirements | GENERAL | no domain named | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;single Cost value for each option | REQUIREMENTS, PROHIBIT |
You manually need to define for each assembly of Components whether the constraint is satisfied. Case Study included latency of streaming (Performance) and Reliability (unspecified), solved by standard set covering solver, I guess |
PROHIBIT | solved by standard set covering solver, I guess | SIMPLE AGGREGATION FUNCTIONS | AF;Acceptable intervals for requirements (utility Functions) | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER LINEAR PROGRAMMING | SET COVERING PROBLEM SOLVER solved with standard solvers. If problem becomes to big | COMPONENT SELECTION | "The target of this approach is COTS Selection even before an architecture is designed. Thus, there are only rough estimations of whether the use of a certain Component will satisfy an overall requirement. The example requirements in the paper are requirements only one Components is responsible for. We might want to exclude this paper, as is does Not have an architectural focus. " | NOT PRESENTED | NOT PRESENTED, NOT NEEDED, NOT PRESENTED |
Manually Added, |
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| Adomi06AABBCCCDF | The MAIS approach to web service design | GENERAL | GENERAL | RUN-TIME | GENERAL | General;No details given | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF with utility Functions | LINEAR MIXED INTEGER | EXACT | EXACT STANDARD | MIXED-INTEGER LINEAR PROGRAMMING (MILP) | SERVICE SELECTION | NOT PRESENTED | Not given | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Tahaee10J | A Polynomial Algorithm for Partitioning Problems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AREA, COST, PERFORMANCE |
Area, Cost, Performance; | AREA, COST, PERFORMANCE, PROHIBIT |
The problem formulation is parametric so that constraints and objectives are mixed, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF;Simple additive functions for objectives | LINEAR INTEGER | Integer decision variables are altered. | EXACT, APPROXIMATIVE |
EXACT STANDARD, WITH GUARANTEE |
INTEGER LINEAR PROGRAMMING, PROBLEM SPECIFIC WITH GUARANTEE |
Exact solutions are claimed for 75% practical cases, specific relaxation for other cases | PARTITIONING | MATHEMATICAL PROOF | Mathemtically prove the exact results | NOT PRESENTED | For quality not needed for exact part | ||||||||
| Aleti09BGM | ArcheOpterix: An Extendable Tool for Architecture Optimization of AADL Models | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | GENERAL | General;Data Communication Overhead, Data Transmission Reliability | MAPPING, MEMORY, PROHIBIT |
Localization, collocation and Memory, Infeasible solutions are ignored in population generation and Evolutionary operators. |
PROHIBIT | Infeasible solutions are ignored in population generation and Evolutionary operators. | MODEL BASED | MB;Models Not Presented | NONLINEAR INTEGER | Deployment problem | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | TODO: in what sense guided? | ALLOCATION | EXPERIMENTS | Experiments | NOT PRESENTED | ||||||||
| Islam06LS | Dependability Driven Integration of Mixed Criticality SW Components | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
"Fault Tolerance Communication Constraints, Schedulability;Has introduced Models for computation of each attribute." | GENERAL, REPAIR |
MULTIPLE-> GENERAL. Binding (processor),Dependability,Computing,Communication,Timing, Back tracking is used to Repair the solutions. |
REPAIR | Back tracking is used to Repair the solutions. | MODEL BASED | MB;Has Not described the Models in the paper. Only referenced | NONLINEAR INTEGER | Deployment | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Guided Heuristics | ALLOCATION | """- Good paper - presents an approach of Deployment optimization""" | INDUSTRIAL CASE STUDY | Break-by-wire system | NOT PRESENTED | |||||||
| Malek07 | A User-Centric Framework for Improving a Distributed Software System’s Deployment Architecture | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE |
Latency, Durability;Not Presented in this paper | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Models Not Presented | NONLINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | ALLOCATION | Does Not give sufficient information. May need to refer other detailed papers on the contribution. | NOT PRESENTED | Not Presented | NOT PRESENTED | |||||||||||
| Mikic-Rakic05MM | Improving Availability in Large, Distributed Component-Based Systems Via ReDeployment | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | AVAILABILITY | Availability; | PROHIBIT, MEMORY, MAPPING |
Not clear, but seems to be Prohibited, , Localization and collocation |
PROHIBIT | Not clear, but seems to be Prohibited | MODEL BASED | MB;They have presented a formula to calculate the Availability from HW/SW integrated Model | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Avala Algo | ALLOCATION | Good paper on overall Deployment optimization | EXPERIMENTS | Experiments | COMPARISON WITH EXACT ALGORITHM | Compared the Avala with Exact, biased and unbiased algorithms | |||||||
| Nicholson98 | Selecting a Topology for Safety-Critical Real-Time Control Systems | EMBEDDED SYSTEMS | Topology Selection of embedded systems, topology refers to configuration decisions in both HW and SW | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
dependability, timing;Dependability : at architecture level, Timing : Configuration level | MAPPING, DEPENDABILITY, MEMORY, PROHIBIT |
WCRT, dependability targets, capacity constraints, restrictions on unit to unit assignments, This was CAPACITY Before, but looks the same as memorty constraint., |
PROHIBIT | MODEL BASED | MB;Presents a set of Models for each quality attribute and presents the approach in General way | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Genetic algorithm, Tabu search and simulated annealing | HARDWARE SELECTION, ALLOCATION |
topology configuration | INDUSTRIAL CASE STUDY, EXPERIMENTS |
COMPARISON WITH EXACT ALGORITHM | |||||||||
| Qiu99P | Dynamic Power Management Based on Continuous-Time Markov Decision Processes | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | Energy;Power policy optimization for minimum energy consumption | PERFORMANCE, PROHIBIT |
Minimum Performance constraints, Greedy algorithm that Prohibits the constraints |
PROHIBIT | Greedy algorithm that Prohibits the constraints | MODEL BASED | MB;CTMC | NONLINEAR CONTINOUS | Decision variable :delay Weight | EXACT | EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC |
LINEAR PROGRAMMING, GREEDY |
Change the power policy until best power is achieved. Based on a policy optimisaltion paper from 1968, Dynamic approach, no optimisation problem? |
SOFTWARE PARAMETERS | "policy optimization strategy for Dynamic power management (Not specific to software), * System elements Service Provider, power Manager, Service Requester, Service Request Queue - CTMC Models of the PM System, and environment * Which policy you save max strategy : Increase the delay Weights. to find the optimal set of state-action pairs for the PM such that expected power consumption is minimized Validation : Portable System Case Study" | INDUSTRIAL CASE STUDY | Portable system Case Study | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Seo07MM | An Energy Consumption Framework for Distributed Java-Based Software Systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | GENERAL | ENERGY | Energy;EC | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Simple additive Models | LINEAR INTEGER | All decesion variations are integer options | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | ALLOCATION, COMPONENT SELECTION, SCHEDULING |
Energy estimation framework | EXPERIMENTS | Compared experimental EC with actual | NOT PRESENTED | ||||||||||
| Sharma08J | Deploying Software Components for Performance | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Performance;Response Time optimization | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;DTMC based approach | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Put next Component on servers based on util of servers. | ALLOCATION | EXPERIMENTS | Compared with the experimental results | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compared with the experimental results | ||||||||||
| Simunic00BGD | Dynamic Power Management for Portable Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | Energy;EC | PERFORMANCE, PROHIBIT |
PROHIBIT | MODEL BASED | MB;Time Indexed SMDP | LINEAR CONTINOUS | Parameter changes | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | Heuristics algorithm | SOFTWARE PARAMETERS | "- time indexed SMDP Model - policy optimisation in DPM - Trade of power and Performance" | SIMPLE EXAMPLE | mediaBench Example | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Suri10JHPIS | A software integration approach for designing and assessing dependable embedded systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | dependability in General; | PERFORMANCE, PROHIBIT |
Scheduling length, Infeasible solutions are ignored |
PROHIBIT | Infeasible solutions are ignored | MODEL BASED | Models are used to quantify scheduling and fault containment.; | NONLINEAR INTEGER | Clustering problem, which is Integer | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | They suggest several heuristics how to allocate FCMs to HW | CLUSTERING, ALLOCATION |
EXPERIMENTS | Example and Experiments | NOT PRESENTED | ||||||||
| Hamza-Lup08ASI | Component Selection strategies based on system requirements’ dependencies on Component attributes | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Performance ;Metrics are Latency, used number of gates, power consumption | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;first map system Performance requirements onto Component characteristics (regression or AI), then select the best Components | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Greedy Search+ Backtracking | COMPONENT SELECTION | Network-on-chip example. Quality values for Components are determined from a number of results of a Performance simulation, Performance contribution of each Component is learned and then used in the optimisation. | ACADEMIC CASE STUDY | Simplified example of NoC architecture | NOT PRESENTED | ||||||||||
| Serban09VP | A New Component Selection Algorithm Based on Metrics and Fuzzy Clustering Analysis | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | not explained how, just a function named CriteriaBasedBesstClusterCandidate selection | DESIGN-TIME | COST | Cost, Reusability;Sum of Component Cost, 2 Reusability metrics (used services / offered services, available required services / total required services) per Component, the smaller the first value the more reusable (=better) the Component (weird metric), the larger the second value the more reusable | REQUIREMENTS, NOT PRESENTED |
Weak, only states whether a single Component satisfies a system-wide requirements, no compositionality at all. No required services considered., |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF; | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Clustering | COMPONENT SELECTION | Component selection | ACADEMIC CASE STUDY | Small artificial Case Study | INTERNAL COMPARISSON | Comparison with their previous approaches | |||||||
| Vescan08G | A Hybrid Evolutionary Multiobjective Approach for the Component Selection Problem | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost, number of Components, system requirements;Sum of compoent Cost | REQUIREMENTS | Weak, only states whether a single Component satisfies a system-wide requirements, no compositionality at all. No required services considered. | SIMPLE AGGREGATION FUNCTIONS | AF;simple | NONLINEAR INTEGER | select set of Components, each satisfying some reqs, to satisfy all system reqs | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM, GREEDY, HYBRID |
Greedy search after applying a genetic operator | COMPONENT SELECTION | SIMPLE EXAMPLE | Simple constructed example, 3 Experiments with varying Parameters | NOT PRESENTED | Not Presented | |||||||||
| Vescan08Thesis | Construction Approaches for Component-Based Systems | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;Sum of Component Cost, more software metrics (Reusability and others) | REQUIREMENTS, PROHIBIT |
Weak. Simple composition: required services of a used Component must also be fulfilled in addition to system requirements, Always fulfilled as encoded in genome |
PROHIBIT | Always fulfilled as encoded in genome | SIMPLE AGGREGATION FUNCTIONS | AF;simple | NONLINEAR INTEGER | select set of Components, each satisfying some reqs, to satisfy all system reqs | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, METAHEURISTIC |
BRANCH AND BOUND, EVOLUTIONARY ALGORITHM, GREEDY |
COMPONENT SELECTION | ACADEMIC CASE STUDY | Three case studies, one of them real world (airport problem) No validation of the metrics | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | For quality not needed for exact part, comparison Greedy and branch & bound, comparison Greedy and EA | ||||||||
| Vescan09 | A Metrics-based Evolutionary Approach for the Component Selection Problem | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Weighted sum of several objectives | DESIGN-TIME | COST | Cost, Reusability;Sum of Component Cost, 2 Reusability metrics (used services / offered services, available required services / total required services) per Component, the smaller the first value the more reusable (=better) the Component (weird metric), the larger the second value the more reusable | REQUIREMENTS, PROHIBIT |
Weak, only states whether a single Component satisfies a system-wide requirements, no compositionality at all. No required services considered., Always fulfilled as encoded in genome |
PROHIBIT | Always fulfilled as encoded in genome | SIMPLE AGGREGATION FUNCTIONS | AF;simple | NONLINEAR INTEGER | select set of Components, each satisfying some reqs, to satisfy all system reqs | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | SIMPLE EXAMPLE | Simple constructed example. No validation of the Reusability metric | NOT PRESENTED | Not Presented | |||||||
| Dhakal08PH | Maximizing Service Reliability in Distributed Computing Systems with Random Failures: Theory and Implementation | EMBEDDED SYSTEMS | distributed computing systems (DCS) | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | RELIABILITY | Reliability (load balancing);probability of successfully serving all the Tasks queued at the server nodes + the number of Tasks to be relocated (under the Dynamic view) | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;defined with a set of equations over an intuition of a Model | NONLINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | Analytical solution | ALLOCATION | BENCHMARK PROBLEMS | COMPARISON WITH EXACT ALGORITHM, NOT PRESENTED, COMPARISSON WITH EXACT ALGORITHM |
Comparison with MC based exhaustive search, For quality of the solutions |
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| Simunic01BD | Energy-Efficient Design of Battery-Powered Embedded Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | Energy;EC | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;additive Models +Simulation | NONLINEAR INTEGER | Compiler optimization | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | compiler optimization | OTHER PROBLEM SPECIFIC | "Model Transformation: - Cycle accurate Models for energy consumption - Simulation Based Evaluation - Includes a Model for battery" | INDUSTRIAL CASE STUDY | "SmartBadge portable device Sony Vaio laptop HDD WLAN card" | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Elegbede01A | Availability Allocation to Repairable systems with genetic algorithms:a multi-objective formulation | GENERAL | General Approach | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | multi objective optimization with Weighted sums. | DESIGN-TIME | AVAILABILITY, COST |
Availability,Cost; | GENERAL, PENALTY |
constraints in objective Functions and failure,Repair rates of subsystems, A specific penalty Function has been used |
PENALTY | A specific penalty Function has been used | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;The objectives are described as Functions, Not SAFs | NONLINEAR CONTINOUS | Not very clear | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | GA with Weighted sums. Expressive. Contains Parameter sensitivity analysis as well. | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
change number of Components in sub systems, failure rate, Repair rates. | EXPERIMENTS | NOT PRESENTED | Has given good literature references to justify the algorithm Selection | |||||
| Liang10L | Multi-objective redundancy allocation optimization using a variable neighborhood search algorithm | GENERAL | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY, COST, WEIGHT |
Reliability, cost, Weight; | WEIGHT, VOLUME, PROHIBIT, REDUNDANCY LEVEL |
PROHIBIT | GENERAL, SIMPLE AGGREGATION FUNCTIONS |
General;, SAF;Simple additive functions for reliability |
NONLINEAR INTEGER, LINEAR INTEGER |
Redundancy allocation, Redundancy level |
APPROXIMATIVE | METAHEURISTIC | VARIABLE NEIGHBOURHOOD SEARCH | Similar to Simulated annealing | COMPONENT SELECTION, HARDWARE REPLICATION |
redundancy allocation, Redundancy |
SIMPLE EXAMPLE, BENCHMARK PROBLEMS |
Three examples, |
COMPARISON WITH BASELINE HEURISTIC ALGORITHM | NSGAII, ACO,TS etc. are compared. OPTVAL:COMPARISSON WITH EXACT ALGORITHM entry removed. | ||||||||
| Potena07 | Composition and Tradeoff of Non-Functional Attributes in Software Systems: Research Directions | GENERAL | General Approach | MULTI-OBJECTIVE OPTIMIZATION | Satisfaction of quality attributes minimizing the Cost | DESIGN-TIME | COST | Cost; | RELIABILITY, DELIVERY TIME, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Simple additive formulas for Reliability, time and Cost. | LINEAR INTEGER | Discrete Cost levels, so seems to be Integer problem | NOT PRESENTED | NOT PRESENTED | NOT PRESENTED | Refer DEER framework for optimization | COMPONENT SELECTION | includes build or buy decision as well | NOT PRESENTED | NOT PRESENTED | ||||||||
| Edwards09GTPMSP | Architecture-Driven Self-Adaptation and Self-Management in Robotic Systems | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | , |
GENERAL | Define meta-level components that enable various transformation operators in software components. (deployment, configuration etc). | INDUSTRIAL CASE STUDY | A case study of a team of autonomous mobile robots. In between academic and industrial case study | , , |
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| Esfahani10KM | Taming Uncertainty in Self-Adaptation through Possibilistic Analysis | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Mutltiple utility functions are combined | RUN-TIME | GENERAL | Response time, reliability are examples | , |
SOFTWARE PARAMETERS, HARDWARE PARAMETERS |
, , |
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| Rezaie10NM | A Multi-Objective Particle Swarm Optimization for Web Service Composition | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | RUN-TIME | COST, PERFORMANCE |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | APPROXIMATIVE | METAHEURISTIC | PARTICLE SWARM | SERVICE SELECTION | EXPERIMENTS | COMPARISON WITH BASELINE ALGORITHM | ||||||||||||||||
| Wiesemann08HK | A Stochastic Programming Approach for QoS-Aware Service Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
Duration, Cost, Availability, and Reliability; | QOS VALUES, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF | LINEAR MIXED INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC |
MIXED-INTEGER LINEAR PROGRAMMING (MILP), STOCHASTIC PROGRAMMING |
SERVICE COMPOSITION | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | For quality not needed for exact part | |||||||||||
| Alighanbari06KH | Coordination and Control of Multiple UAVs with Timing and Loitering | EMBEDDED SYSTEMS | assignment of Tasks to Unmanned Aerial Vehicles | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time;Completion of the mission | TIMING, PENALTY |
e.g., must assign three UAVs to strike a target from three different directions within 2 seconds of each other, |
PENALTY | SIMPLE AGGREGATION FUNCTIONS | AF;Summation of times needed for Task Completion | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT STANDARD, PROBLEM-SPECIFIC HEURISTIC, METAHEURISTIC |
MIXED-INTEGER LINEAR PROGRAMMING (MILP), OTHER PROBLEM SPECIFIC, TABU SEARCH |
ALLOCATION | SIMPLE EXAMPLE | INTERNAL COMPARISSON | the three discussed algorithms compared with each other | ||||||||||
| Hashemi09G | Throughput-Driven Synthesis of Embedded Software for Pipelined Execution on Multicore Architectures | EMBEDDED SYSTEMS | streaming applications. Task assigment support for dualcore ES (supports heterogenous processors and different on-chip communication strategies), extension to more cores | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Throughput;Maximise pipeline Throughput | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF?;Simplified Performance MBon computation workload, interprocessor communication (sum, maximum of the two cores). Based on acyclic graph representation | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, WITH GUARANTEE, PROBLEM-SPECIFIC HEURISTIC |
GRAPH PARTITIONING, GRAPH PARTITIONING WITH GUARANTEE, EXACT GRAPH PARTITIONING |
APPROX - PROBLEM SPECIFIC : GRAPH BIPARTITIONING 2 algorithms for dualcore 1) guaranteed optimal, new entry to reflect that they present several graph partitioning algorithms that are in different high level categories (exact, approx). , EXACT - PROBLEM SPECIFIC |
ALLOCATION | they call it Task assignment. scheduling is Not considered, it is assumed to be defined already. No nonconvex cuts (first one core, then the other, than back to the first in the pipeline) | EXPERIMENTS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | For quality not needed for exact part, quantification of the advantage by experimental comparison to other approach (StreamIt Task Assignment by Gordon): measurements of real Hardware (FPGAs) | |||||||||
| Al-naeem05ARB | A Quality-Driven Systematic Approach for Architecting Distributed Software Applications | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Weighted sum of objectives | DESIGN-TIME | RELIABILITY, GENERAL |
COST, GENERAL, DELIVERY TIME |
, expected time required to implement a candidate design |
GENERAL | SIMPLE AGGREGATION FUNCTIONS | LINEAR INTEGER | EXACT | However algorithm is not clear | EXACT PROBLEM-SPECIFIC | Algorithm was not very clear | GENERAL | ACADEMIC CASE STUDY | NOT PRESENTED, NOT NEEDED, NOT PRESENTED |
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| Malek12MM | An Extensible Framework for Improving a Distributed Software System’s Deployment Architecture | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL, PERFORMANCE, RELIABILITY, ENERGY |
Formulas for Availability,Latency,Communication security, Energ Consumption , |
MEMORY, PROHIBIT, MAPPING |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | Simple aggregate functions are given for the quality attributes | LINEAR MIXED INTEGER | APPROXIMATIVE | Compare different optimisation algorithms | GENERAL | Compare more than one optimisation algorithms (MILP, Greedy, GA) | ALLOCATION | ACADEMIC CASE STUDY | NOT PRESENTED, , |
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| Chen06GS | Architecture-based Self-Adaptation in the Presence of Multiple Objectives | GENERAL | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | PROBLEM-SPECIFIC HEURISTIC | Could be some tactics | , , |
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| Ahmed10M | Concept-Based Partitioning for Large Multidomain Multifunctional Embedded Systems | EMBEDDED SYSTEMS | Cosynthesis in Embedded systems | MULTI-OBJECTIVE OPTIMIZATION | Generally consider the presence of multiple objectives | DESIGN-TIME | GENERAL | General;General quality attributes to evaluate an allocation | GENERAL, PROHIBIT |
Not specifically mentioned, |
PROHIBIT | MODEL BASED | MB;Models are used to evaluate quality. The models are not presented in the paper | NONLINEAR INTEGER | Allocation/clustering problem, which is in integer space | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | An algorithm they call concept-based design | PARTITIONING | Can be put in to clustering category | INDUSTRIAL CASE STUDY | case study of UAV | NOT PRESENTED | ||||||
| Dai07L | Optimal Resource Allocation for Maximizing Performance and Reliability in Tree-Structured Grid Services | INFORMATION SYSTEMS | Resource Management systems in Grid/Cloud services | MULTI-OBJECTIVE OPTIMIZATION | GENERAL | For Grid systems, its hard to distinguish design or run time | PERFORMANCE, RELIABILITY |
Performance,Reliability;Inter-relationship between the two attributes is considered. | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Use graph structured Model and derive Mathematical formulas for Reliability and Performance | NONLINEAR CONTINOUS | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Fitness base ranking. TODO: Is it also guided? | HARDWARE SELECTION, HARDWARE REPLICATION |
the whole approach is based on construction of tree structure. | ACADEMIC CASE STUDY | Grid service system example | NOT PRESENTED | ||||||||
| Greiner03GW | Safety Systems Optimum Design by Multicriteria Evolutionary Algorithms | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST |
Cost, Unavilability; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Fault trees are constructed | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SPEA2, NSGAII and controlled elitist-NSGAII | HARDWARE SELECTION, MAINTENANCE SCHEDULES, HARDWARE REPLICATION |
REDUNDANCY ALLOCATION, , REDUNDANCY ALLOCATION |
ACADEMIC CASE STUDY | Containment Spray System of a Nuclear Power Plant | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | SPEA2, NSGAII and controlled elitist-NSGAII | |||||||
| Li09CWL | Fast Scalable Optimization to Configure Service Systems having Cost and Quality of Service Constraints | INFORMATION SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | PERFORMANCE, COST |
PERFORMANCE, COST, MAPPING, PROHIBIT |
PROHIBIT | MODEL BASED | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | LINEAR PROGRAMMING, OTHER PROBLEM SPECIFIC |
ALLOCATION, HARDWARE REPLICATION |
EXPERIMENTS | NOT PRESENTED | ||||||||||||||||
| Blickle97 | Theory of Evolutionary Algorithms and Application to System synthesis | EMBEDDED SYSTEMS | Systems synthesis | GENERAL | both single and multi objective approaches are presented | DESIGN-TIME | COST, PERFORMANCE |
Performance,Cost; | GENERAL, PENALTY, REPAIR |
Generally present how to deal with constraints with different algorithms, Both methods are presented |
PENALTY, REPAIR |
Both methods are presented | SIMPLE AGGREGATION FUNCTIONS | AF;dependence graphs are used, but Functions are simply the additions | LINEAR INTEGER | Deployment and ordering problem. | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | This is a phd Thesis. Has considered many optimization algorithms and their capabilities. | ALLOCATION, Scheduling |
constructs the system synthesis with the optimization, |
EXPERIMENTS, INDUSTRIAL CASE STUDY |
Case Study of video codec | NOT PRESENTED | |||||
| Skroch10 | Multi-criteria Service Selection with Optimal Stopping in Dynamic Service-Oriented Systems | INFORMATION SYSTEMS | SOA | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;The QoS of components can be determined more complex, but this is at runtime, so the observed values or reported values are used and e.g. summed up | NONLINEAR INTEGER | selection of services | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | RESTRICTED ENUMERATION OF ALL POSSIBLE SOLUTIONS, EXHAUSTIVE SEARCH |
Starts exhaustive search with a defined time frame per service to select. Stops after time is up and takes the best | SERVICE SELECTION | EXPERIMENTS | artificial Web services | NOT PRESENTED | |||||||||||
| Lukasiewycz08GHT | Efficient Symbolic Multi–Objective Design Space Exploration | EMBEDDED SYSTEMS | Design space exploration, focus on optimisation technique, so it could be applied to other domains, too | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | Design Space Exploration | GENERAL | Custom fitness Function;For the Exact method, it has to be a Linear Function. Example are area and power consumption | STRUCTURAL, NOT PRESENTED |
design must be feasible: Mapping of a communication link must be between the nodes to which the communicationg Tasks have been mapped. , encoded in Exact solution approach |
NOT PRESENTED | encoded in Exact solution approach | SIMPLE AGGREGATION FUNCTIONS | AF;sum of the Cost of the mapping edges and resources. Based on graph based Models | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
INTEGER LINEAR PROGRAMMING | Two approaches are presented 1) a Heuristics to use single-objective Pseudo Boolean solvers for the Nonlinear MO Case, and 2) a new Pseudo Boolean solver for MO. | HARDWARE SELECTION, ALLOCATION |
Selection of resources is to choose from a predefined set of possibilities. | INDUSTRIAL CASE STUDY, EXPERIMENTS |
industrial example from automotive area, Experiments with generated problems | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | For quality not need for exact part, Comparison with SPEA2 | |||||
| Saxena10K | MDE-Based Approach for Generalizing Design Space Exploration | GENERAL | Presents an MDE-based approach which is claimed be applicable to any domain | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Consider multiobjectives, transfromed into one | DESIGN-TIME | GENERAL | General;Optimizable attributes in general | GENERAL, PROHIBIT |
General boolean constraints, Does not describe any panelty or Repair functions |
PROHIBIT | Does not describe any panelty or Repair functions | SIMPLE AGGREGATION FUNCTIONS | SAF;Evaluation models are not described. | LINEAR INTEGER | Considers the integer design objectives | GENERAL | GENERAL | GENERAL | Intermediate language Minizinc. | GENERAL | Presents an abstract view of transformation operators. | INDUSTRIAL CASE STUDY | Case Study on software product line configuration | NOT PRESENTED | |||||
| Aneja04CN | Minimal-Cost System Reliability With Discrete-Choice Sets for Components | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST | Cost;aystem Cost is sum of Component Cost | RELIABILITY, NOT PRESENTED |
system Reliability is determined from independent Component reliabilities under the assumption that k out of n Components must work for the system to work, |
NOT PRESENTED | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | LINEAR INTEGER | EXACT | EXACT PROBLEM-SPECIFIC | OTHER EXACT PROBLEM SPECIFIC | an Exact method is given | COMPONENT SELECTION | discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Aydin01MMM | Dynamic and Aggressive Scheduling Techniques for Power-Aware Real-time Systems | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | Energy;EC | PERFORMANCE, PROHIBIT |
Scheduling, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | AF;AF, Strickly increasing polynomial, with at least 2nd degree | NONLINEAR INTEGER | Decision variable is the CPU time Allocation | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
OTHER EXACT PROBLEM SPECIFIC, OTHER PROBLEM SPECIFIC |
Exact offline part, the rest in online (and does Not fit in our table?) Aggressive Greedy Heuristics, |
SOFTWARE PARAMETERS | "-Voltage Scaling - assumes a Function g(s) for power consumption of a processor under speed s. - Address the problem of optimizing power while preserving deadlines. - Instead of focus on WCET, use the advantage of real data. (in adaptive strategy)" | SIMPLE EXAMPLE | Mathematical formulations and Example. | NOT PRESENTED | For quality not needed for exact part | |||||||
| Coit06K | Multiple Weighted Objectives Heuristics for the Redundancy Allocation Problem | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | the problem is originally formulated as SO, but then transformed to MOO as a solution strategy | DESIGN-TIME | RELIABILITY | Reliability;maximize mission time Reliability | COST, WEIGHT, PROHIBIT |
Weight = sum of Component Weights; Cost = sum of Component Cost, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | Redundancy alloaction | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | MWO (Multiple Weighted objectives) Heuristics (maximize Reliability of each subsystem) | HARDWARE SELECTION, SOFTWARE SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION |
discrete set of Component choices | SIMPLE EXAMPLE | application to software engineering Not considered | NOT PRESENTED | ||||||
| Hassine06MI | A Constraint-Based Approach to Horizontal Web Service Composition | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | Abstract;Called soft constraints | GENERAL, PENALTY |
called hard constraints, for soft constraints |
PENALTY | for soft constraints | SIMPLE AGGREGATION FUNCTIONS | AF;Sum up user preference and substract constraint violation? | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | custom implementation of a constraint optimization problem (COP) algorithm with two kinds of constraints: hard and soft constraints | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | ACADEMIC CASE STUDY | Academic Case Study (similar to the Travel Planer Case Study) | NOT PRESENTED | ||||||||
| Hong99KQPS | Power Optimization of Variable Voltage Core-Base systems | EMBEDDED SYSTEMS | Scheduling problem | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | Energy;EC | PERFORMANCE, PROHIBIT |
Scheduling constraints, |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | AF;Sum of non-Linear formulaes. So canNot label as SAF | LINEAR MIXED INTEGER | Component Selection like problem, which selects the processor core together with Parameter tuning | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | Exhaustive searches plus Heuristics algorithm | HARDWARE SELECTION, HARDWARE PARAMETERS |
"- Develop scheduling technique that treat Voltage as a variable to be determined - synthesis technique, also Address the Selection of processor core and instruction" | INDUSTRIAL CASE STUDY | Demonstrate the approach on variety of modern industrial-strength multimedia and communication applications | NOT PRESENTED | |||||||
| Mabrouk09BKGI | QoS-Aware Service Composition in Dynamic Service Oriented Environments | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | AVAILABILITY, COST, PERFORMANCE, RELIABILITY |
Response Time, Reliability, Availability, Throughput, Cost; | QOS VALUES, NOT PRESENTED |
NOT PRESENTED | SIMPLE AGGREGATION FUNCTIONS | AF;Simple AF(sum, product, min, max, average) | LINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | SERVICE SELECTION, OTHER PROBLEM SPECIFIC |
Service Selection, Service Orchestration | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | |||||||||||
| Manoj09SM | A state-space search approach for optimizing reliability and cost of execution in distributed sensor networks | EMBEDDED SYSTEMS | Distributed sensor networks | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | summation of the reliability, execution, and other costs | DESIGN-TIME | RELIABILITY, ENERGY |
Reliability, execution costs;… plus performance, which influences both | PHYSICAL, PROHIBIT |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | SAF; | NONLINEAR INTEGER | EXACT, APPROXIMATIVE |
EXACT PROBLEM-SPECIFIC, PROBLEM-SPECIFIC HEURISTIC |
OTHER PROBLEM SPECIFIC, OTHER EXACT PROBLEM SPECIFIC |
A* and greedy A* algorithm, |
ALLOCATION | SIMPLE EXAMPLE | INTERNAL COMPARISSON | only the two designed algorithms were compared with each other | |||||||||
| Billionnet08 | Redundancy Allocation for Series-Parallel Systems Using Integer Linear Programming | GENERAL | Redundancy Allocation problem in General | SINGLE-OBJECTIVE OPTIMIZATION | Reliability optimization | DESIGN-TIME | RELIABILITY | Reliability;Reliability optimization | COST, WEIGHT, PROHIBIT |
, Infeasible solutions are disregareded in the optimization |
PROHIBIT | Infeasible solutions are disregareded in the optimization | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF;Series parellel systems are used. More appropriate to consider as a Function rather a Model | NONLINEAR INTEGER | Integer Linear programming is used. | APPROXIMATIVE | WITH GUARANTEE | APPROX INTEGER LINEAR PROGRAMMING WITH GUARANTEE | HARDWARE REPLICATION, SOFTWARE REPLICATION |
Redundancy allocation based on the module concept, and non-identical components notion. So applicable to both. | EXPERIMENTS | random Experiments | NOT PRESENTED | ||||||
| Eames09NS | DesertFD: a finite-domain constraint based tool for design space exploration | EMBEDDED SYSTEMS | General framework for design space exploration in ES: e.g. latency-driven component selection and mapping in signal/image processing, certain classes of parameter-based analysis in SoC design, and library-based FPGA application integration. | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Can be in MTS (optimise utility of soft constraints) or MOO mode (optimise soft constraints) | DESIGN-TIME | GENERAL | Custom functions;Can be defined | GENERAL, PROHIBIT |
any qualty can be either marked as hard constraint (=constraint) or soft constraint (=objective), encoded in constraint problem solver |
PROHIBIT | encoded in constraint problem solver | NON-LINEAR MATHEMATICAL FUNCTIONS | AF;properties have to mathematically composed in a way | NONLINEAR INTEGER | APPROXIMATIVE, EXACT |
WITH GUARANTEE, EXACT PROBLEM-SPECIFIC |
BRANCH AND BOUND BASED WITH GUARANTEE, BRANCH AND BOUND |
added on 2011-10-14 based on paper collection.xml. Mozart solver: finite domain constraint solver, similar to B&B, can also be configured to do an exhaustive search or, if heuristics are available, can be exact, Mozart solver: finite domain constraint solver, similar to B&B, can also be configured to do an exhaustive search or, if heuristics are available, can be exact |
GENERAL | Design options modelled as an AND-OR-Tree, so any change can be added. Must be structured hierarchically, though | ACADEMIC CASE STUDY | two case studies, I cannot assess their complexity. Could even be real world, but they do not say it explicitly | INTERNAL COMPARISSON | artifiical experiments to analyse scalability, e.g. how well it handles large problem instances. Or is that validation of approach? | |||||
| Youness09HSTISWM | Optimization Method for Scheduling Length and the Number of Processors on Multiprocessor Systems | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time; | PRECEDENCE, PHYSICAL, PROHIBIT |
PROHIBIT | NON-LINEAR MATHEMATICAL FUNCTIONS | NMF; | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | combination of the A* algorithm and geometric algorithm, with some tuning | ALLOCATION, SCHEDULING |
BENCHMARK PROBLEMS | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | compared with BNP algorithms | |||||||||||
| Esfahani11KM | Taming Uncertainty in Self-Adaptive Software | EMBEDDED SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | GENERAL | any qunaitifiable non-functional properties | GENERAL, PROHIBIT |
General definition of constraints over configuration space, |
PROHIBIT | SIMPLE AGGREGATION FUNCTIONS | LINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | A greedy search | SOFTWARE PARAMETERS | ACADEMIC CASE STUDY | INTERNAL COMPARISSON, NOT NEEDED, |
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| Amari10D | Redundancy Optimization Problem with Warm-Standby Redundancy | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, WEIGHT, VOLUME, PROHIBIT |
PROHIBIT | MODEL BASED | MB; | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | COMPONENT SELECTION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Andrews03B | Using statistically designed Experiments for safety system optimization | EMBEDDED SYSTEMS | Safety Critical System | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | Safety ;In this Case its system unavaiability | COST, AVAILABILITY, PENALTY |
Penalty Function, System down time, |
PENALTY | MODEL BASED | MB;BDD Diagrams but no Fault Tree | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | RESTRICTED ENUMERATION OF ALL POSSIBLE SOLUTIONS | (Factorial) assume a certain independence of the decision variables, and then do a factorial analysis. So this is some kind of pruned exhaustive search; pruned by factorial design. Not exact because independence must not hold | HARDWARE SELECTION, HARDWARE REPLICATION, MAINTENANCE SCHEDULES |
Redundancy allocation, Component selection, Maintenance schedules | ACADEMIC CASE STUDY | High-integrity protection system | NOT PRESENTED | ||||||||
| Andrews04B | A branching search approach to safety system design optimisation | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | SAFETY | Safety (system unAvailability), Cost, Maintainance downtime; | DESIGN, PROHIBIT |
exclusion, |
PROHIBIT | MODEL BASED | MB;Fault trees are constructed and then efficiently evaluated with BDDs | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC, BRANCH AND BOUND BASED |
BRANCH AND BOUND INSPIRED ALGORITHM BUT WITH STATISTICAL SAMPLING, Branch and Bound inspired algorithm but with statistical sampling |
HARDWARE SELECTION, HARDWARE REPLICATION, MAINTENANCE SCHEDULES |
Redundancy allocation, Component selection, Maintenance schedules | ACADEMIC CASE STUDY | High-integrity protection system | NOT PRESENTED | |||||||
| Banerjee04N | Efficient Search Space Exploration for HW-SW Partitioning | GENERAL | HW SW partitioning | SINGLE-OBJECTIVE OPTIMIZATION | Single objective with hard constraints | DESIGN-TIME | PERFORMANCE | Performance;Execution time | AREA, PROHIBIT |
HW area constraint (Not clear what it really mean), |
PROHIBIT | MODEL BASED | MB;Execution Graphs to evaluate Performance, and compute like an additive Function. Can put in to MBcategory | LINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | customized simulated annealing with local search | ALLOCATION | EXPERIMENTS | a series of Experiments have been conducted | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compared with KLFM algorithm and showed 10% better than that | |||||||
| Benazouz10MMU | A New Method for Minimizing Buffer Sizes for Cyclo-Static Dataflow Graphs | GENERAL | streaming applications, in general | SINGLE-OBJECTIVE OPTIMIZATION | Buffer Size minimization | DESIGN-TIME | PERFORMANCE | Buffer Size;Minimize the buffer size under trhougput constraints | PERFORMANCE, PRECEDENCE, REPAIR |
Throughput, Precedence, |
REPAIR | MODEL BASED | MB;Cyclo-static and synchronous data flow graphs | NONLINEAR MIXED INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | BRANCH AND BOUND BASED, GRAPH PARTITIONING, BRANCH AND BOUND |
Branching algorithm with backtracking, Branching algorithm with backtracking, additional graph partitioning heuristoc |
SCHEDULING, SOFTWARE PARAMETERS |
Change the buffer sizes, Precedence of the buffers | INDUSTRIAL CASE STUDY | Reed Solomon Decoder | MATHEMATICAL PROOF | |||||||
| Benini98HS | System-level Power Estimation And Optimization | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | Energy;EC | PERFORMANCE, PROHIBIT |
, The policy avoids unsatisfactory solutions |
PROHIBIT | The policy avoids unsatisfactory solutions | MODEL BASED | MB;Power state machines | NONLINEAR CONTINOUS | The selection of parameters from continous space | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | GREEDY | Dynamic approach | SOFTWARE PARAMETERS | Introduction of the concept of power state machines | NOT PRESENTED | NOT PRESENTED | ||||||||
| Benini98MMPQ | Power Optimization of Core-Based Systems by Address Bus Encoding | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | ENERGY | Energy;EC | PERFORMANCE, PROHIBIT |
Delay, |
PROHIBIT | MODEL BASED | MB;Problem specific evaluation Models | NONLINEAR CONTINOUS | Is Clustering of elements an Integer problem? | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Clustering Heuristics | CLUSTERING | "- Low power Address bus encoding - Application Dependent - Performance constraints - minimize energy" | NOT PRESENTED | NOT PRESENTED | |||||||||
| Blickle98TT | System-Level Synthesis Using Evolutionary Algorithms | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | System level synthesis: Find architecture and map Functionalities on it. | GENERAL | Custom fitness Functions;Fitness Function can be arbitrarily defined for each problem based on the Model. Example given are Cost, data-Throughput, power consumption, maintainability | COST, PERFORMANCE, PENALTY |
and user defined mapping constraints, something is added to fitness if constraint is violated |
PENALTY | something is added to fitness if constraint is violated | MODEL BASED | MB;graphs- problem graph (data flow), architecture graph (Functional resources, busses), chip graph, specification graph (for constraints and the solution mapping) | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | uses Repair Heuristics to handle infeasible individuals. Uses a scheduling Heuristics | HARDWARE SELECTION, ALLOCATION, SCHEDULING |
Selection of architecture (Allocation), mapping of Functionality to architecture (binding), scheduling | INDUSTRIAL CASE STUDY | real world, architecture for H.261 video codec | NOT PRESENTED | |||||||
| Boone10HSJJTDD | SALSA: QoS-aware load balancing for autonomous service brokering | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | PERFORMANCE | Response Time, Waiting Time; | PERFORMANCE, PENALTY |
Handled with Penalty Functions IM : ServerOverlaoding->Performance, |
PENALTY | MODEL BASED | MB; M/M/1 queueing network | NONLINEAR CONTINOUS | APPROXIMATIVE | METAHEURISTIC | SIMULATED ANNEALING | PARTITIONING | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | comparison with round robin based Allocation | ||||||||||
| Castro10LB | Reducing Memory Requirements of Stream Programs by Graph Transformations | EMBEDDED SYSTEMS | Multiprocessor system on chip | SINGLE-OBJECTIVE OPTIMIZATION | Memory footprint reduction | DESIGN-TIME | PERFORMANCE | Memory/Size; | PERFORMANCE, PROHIBIT |
Parallism constraints, |
PROHIBIT | MODEL BASED | MB;Cyclo-static and synchronous data flow graphs | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER LINEAR PROGRAMMING | CLUSTERING, SCHEDULING |
Graph transformations | BENCHMARK PROBLEMS | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Trcka11HBGS | Integrated Model-Driven Design-Space Exploration for Embedded Systems | EMBEDDED SYSTEMS | GENERAL | Does not specifically mention, but the approach is very generic. | DESIGN-TIME | GENERAL | High level framework. | GENERAL, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | Petri-NEts, Timed automata etc. The support is there for the use of models for evaluating quality attributes from candidate architecture. | NONLINEAR INTEGER | Does not talk about continues options. Not sure. | GENERAL | Provide support for various third party optimisation tools like FORMULA, MATLAB, Java GA implementations. | GENERAL | General framework | GENERAL | INDUSTRIAL CASE STUDY | Multi-function printer design case study and explaining the authors experience | NOT PRESENTED, , |
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| Zheng03W | Heuristics Optimization of Scheduling and Allocation for Distributed Systems with Soft Deadlines | INFORMATION SYSTEMS | Telecommunication systems and similar, bookstore Case Study used | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | meeting soft deadlines;the likelihood to meet soft deadlines defined through percentiles of response time | NOT PRESENTED, NOT PRESENTED |
, does Not apply |
NOT PRESENTED | does Not apply | MODEL BASED | MB;Layered Queueing Networks | NONLINEAR INTEGER | setting Allocation and Task priorities | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | RULE-BASED only constrain satisfaction | ALLOCATION, SCHEDULING |
the scheduling is included via optimizing the set of Task priorities | ACADEMIC CASE STUDY | realistic Case Study & set of randomly generated Example | NOT PRESENTED | ||||||
| Dave97LJ | COSYN: Hardware-Software Co-Synthesis of Embedded Systems | EMBEDDED SYSTEMS | Hardware software Co-synthesis | SINGLE-OBJECTIVE OPTIMIZATION | Some objectives are considered as constrains, Optimize power | DESIGN-TIME | COST, ENERGY, PERFORMANCE |
Performance, Cost, energy;scheduling,energy consumption has been considered | PERFORMANCE, REPAIR |
deadline achievement, If timing constraint is violated, try to reschedule |
REPAIR | If timing constraint is violated, try to reschedule | MODEL BASED | MB;Task and Finite Time Estimation(FTE) graphs | NONLINEAR INTEGER | Allocation problem. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Clustering Heuristics | SCHEDULING, CLUSTERING |
Task Clustering, scheduling | INDUSTRIAL CASE STUDY, LITERATURE COMPARISON |
Comparison with literature and a Case Study on transport system | NOT PRESENTED | |||||
| Dave98J | COHRA: Hardware–Software Co-synthesis of Hierarchical Heterogeneous Distributed Embedded Systems | EMBEDDED SYSTEMS | Hardware software Co-synthesis | MULTI-OBJECTIVE OPTIMIZATION | MOO with hierarchical architectures | DESIGN-TIME | COST, ENERGY, PERFORMANCE, RELIABILITY |
Performance, Reliability,Cost, energy;fault tolerance, low power, Cost etc. are considered as objectives | PERFORMANCE, REPAIR |
deadline achievement, If timing constraint is violated, try to reschedule |
REPAIR | If timing constraint is violated, try to reschedule | MODEL BASED | MB;Task and FTE graphs | NONLINEAR INTEGER | Allocation problem. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Clustering Heuristics | CLUSTERING, SCHEDULING |
Task Clustering, scheduling | INDUSTRIAL CASE STUDY, LITERATURE COMPARISON |
Comparison with literature and a Case Study on transport system | NOT PRESENTED | |||||
| Dick98J | MOGAC: A Multiobjective Genetic Algorithm for Hardware-Software Co-Synthesis of Distributed Embedded Systems | EMBEDDED SYSTEMS | Goal: Synthesize the ES architecture, domain: Hardware-Software co-design. Feature: Allows Multiple CPUs | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | Synthesis ES designs | COST, ENERGY |
Power, Cost; | COST, PENALTY |
Cost acts as an objective as well as a constraint, Serverity of the constraint violations penlize the solutions |
PENALTY | Serverity of the constraint violations penlize the solutions | MODEL BASED | MB;Task graphs for computation of Performance | NONLINEAR INTEGER | Integer decision variables are altered. | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | enhanced by Heuristics for complex types of systems (multi-rate, relatively large hyperperiods). No Repair. | HARDWARE SELECTION, ALLOCATION, SCHEDULING |
Scaling of resources (they call it Allocation): How many processors, how many communication links? Allocation (they call it assignment): Which Task is assigned to which processor? Scheduling: What is the timing of events? | EXPERIMENTS | NOT PRESENTED | ||||||
| ElSayed01CW | Automation Support for Software Performance Engineering | EMBEDDED SYSTEMS | Example is telephone switch | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Performance; | TIMING, PENALTY |
y number of scenarios have deadlines which must be realized some percentage of the time., |
PENALTY | MODEL BASED | MB;Layered Queueing Networks | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | only constrain satisfaction | ALLOCATION | Task Allocation (to processors); More complex Notion of Tasks than in real-time systems | SIMPLE EXAMPLE | NOT PRESENTED | |||||||||
| Farnsworth10BTZ | A Novel Approach to Multi-level Evolutionary Design Optimization of a MEMS Device | EMBEDDED SYSTEMS | MEMS-Micro Electro Mechanical Systems | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Frequency;Passband, Stop band and central frequency | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Mathematical models of the filters | NONLINEAR MIXED INTEGER | Paramters and structure/number of RCL tanks | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGAII | OTHER PROBLEM SPECIFIC | EXPERIMENTS | Experiments for possible ranges of the problem | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compare with other algorithms | ||||||||
| FitzRoyDale09K | Towards automatic performance optimisation of componentised systems | EMBEDDED SYSTEMS | Componentised system architecture with hardware mediated memory (example: networked video player) | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Performance;Several metrics, e.g. throughput | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;System simulation model, can have different abtraction levels (e.g with or without cache) | NONLINEAR INTEGER | NOT PRESENTED | NOT PRESENTED | EXHAUSTIVE SEARCH | COMPONENT SELECTION | Component selection, Connector selection (included in component selecting in a broader sense). It possible to specify custom rules what can be replaced by what | ACADEMIC CASE STUDY | Small Case Study | NOT PRESENTED | ||||||||||
| Galvan07WGSM | New Evolutionary Methodologies for Integrated Safety System Design and Maintenance Optimization | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST |
Cost, Unavilability; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Fault trees are constructed | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA II (Flexible Evolutionary Agent) | COMPONENT SELECTION, MAINTENANCE SCHEDULES, HARDWARE REPLICATION, SOFTWARE REPLICATION |
Redundancy Allocation, , Redundancy Allocation |
ACADEMIC CASE STUDY | Containment Spray System of a Nuclear Power Plant | NOT PRESENTED | ||||||||
| Glass10LHT | Lifetime Reliability Optimization for Embedded Systems: A System-Level Approach | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME, RUN-TIME |
This is different from DT/RT, because this has a design time part and run time part | GENERAL | General;Paper presetenst Reliability and Cost, but allows any quality attribute in General | MAPPING, PROHIBIT |
mapping constraints can be specified. (similar to localization and colocation), |
PROHIBIT | MODEL BASED | MB;BDD s are used to quantify Reliability. Allows any Model in General as I understood | NONLINEAR MIXED INTEGER | Allocation and binding | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Some MOEA from the Pisa framework | ALLOCATION, HARDWARE REPLICATION |
Redundancy Allocation, Deployment | INDUSTRIAL CASE STUDY | Case Study of ACC + BBW | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | experimental results are presented to validate the optimization | ||||||
| Gokhale04a | Cost Constrained Reliability Maximization of Software Systems | GENERAL | Software architecture optimization in General | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, PENALTY |
, Constraint is considered in fitness Function, resulting a penalty efferct |
PENALTY | Constraint is considered in fitness Function, resulting a penalty efferct | MODEL BASED | MB;DTMC based approach | NONLINEAR INTEGER | Component Selection and selecting of Reliability increment options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | INDUSTRIAL CASE STUDY | 3 Case Study are presetned | NOT PRESENTED | no proper validation of the optimization as I see | |||||||
| Gokhale04b | Software Application Design Based On Architecture, Reliability and Cost | GENERAL | Software Reliability optimization in General | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, PENALTY |
, Constraint is considered in fitness Function, resulting a penalty efferct |
PENALTY | Constraint is considered in fitness Function, resulting a penalty efferct | MODEL BASED | MB;DTMC based approach | NONLINEAR INTEGER | Component Selection and selecting of Reliability increment options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | INDUSTRIAL CASE STUDY | An application architecture described as a DTMC | COMPARISON WITH EXACT ALGORITHM | Exhaustive serach has been carried out for the Case Study and compare the EA results | |||||||
| Grunske06 | Identifying Good Architectural Design Alternatives with MultiObjective Optimisation Strategies | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | COST, RELIABILITY |
Reliability,Cost; | WEIGHT, PROHIBIT |
PROHIBIT | MODEL BASED | MB;Reliability Block Diagrams | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
REDUNDANCY ALLOCATION | NOT PRESENTED | NOT PRESENTED | ||||||||||
| Henkel94EHB | Adaptation of Partitioning and High-Level Synthesis in Hardware/Software Co–Synthesis | EMBEDDED SYSTEMS | Partitioning and high-level synthesis of ES | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | Multiple objectives are transformed to single overhead metric using Weighted sum | DESIGN-TIME | AREA, PERFORMANCE |
Performance,area;System execution time and area | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Execution graphs for Performance Modeling | NONLINEAR INTEGER | Allocation/Clustering problem. | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Interface with COSYMA | ALLOCATION | Partitioning | EXPERIMENTS | Compare with Benchmark Problems | NOT PRESENTED | |||||||
| Hou97S | Allocation of Periodic Task Modules with Precedence and Deadline Constraints in Distributed Real-Time Systems | EMBEDDED SYSTEMS | Distributed real-time systems | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE | Completion time;Maximise the probability of meeting time deadlines | PRECEDENCE, PHYSICAL, PROHIBIT |
Physical = each Task (module) is assigned to Exactly one server, |
PROHIBIT | MODEL BASED | MB;the Model is defined in terms of various Functions, but the Functions are combined in quite a complex way (combining both scheduling and Task Allocation and deriving the probability of meeting given time deadlines) | NONLINEAR INTEGER | EXACT | EXACT STANDARD | INTEGER PROGRAMMING ALGORITHM | branch and bound | ALLOCATION, SCHEDULING |
Allocation of Tasks to nodes and scheduling of Tasks assigned to each node | EXPERIMENTS | randomly-generated set of experimental systems | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Izosimov05PEP | Design Optimization of Time- and Cost-Constrained Fault-Tolerant Distributed Embedded Systems | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | PERFORMANCE, RELIABILITY, COST |
Timeliness, Cost, Reliability; | TIMING, COST, PROHIBIT |
PROHIBIT | MODEL BASED | MB;Scheduling | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | TABU SEARCH, GREEDY |
SCHEDULING, ALLOCATION |
EXPERIMENTS | randomly-generated set of experimental systems | INTERNAL COMPARISSON | Comparison between the different algorithms | |||||||||
| Kastner02 | Synthesis Techniques and Optimizations for Reconfigurable Systems | EMBEDDED SYSTEMS | HW SW partitioning | MULTI-OBJECTIVE OPTIMIZATION | but, the formulations are driven by the objectives | DESIGN-TIME | Focus on reconfigurable systems, may do it in runtime as well | AREA, PERFORMANCE |
Performance, area,reconfiguration time;specific ascpects in reconfigurable circuit design | PERFORMANCE, PROHIBIT |
Timing, |
PROHIBIT | MODEL BASED | MB; | NONLINEAR INTEGER | Deployment,scheduling and Clustering all seems Integer problems | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | Specific Heuristics algorithms to optimize the goals, Linear programming? | ALLOCATION, CLUSTERING, OTHER PROBLEM SPECIFIC |
Other(Whether implemented in HW or SW), Clustering and scheduling | EXPERIMENTS | Experiments for each aspect of optimization | NOT PRESENTED | Compared with benchmark problems in the domain | ||||
| Kim06K | HW/SW Partitioning Techniques for Multi-Mode Multi-Task Embedded Applications | EMBEDDED SYSTEMS | Multi-Mode and Multi-Task embedded applications | SINGLE-OBJECTIVE OPTIMIZATION | Minimize Cost satisfying Timing | GENERAL | COST | Cost;Implementation and Hardware Cost | PERFORMANCE, PROHIBIT |
Execution time constraints, |
PROHIBIT | MODEL BASED | MB;Task graphs for computation of Performance | NONLINEAR INTEGER | mapping and Allocation problem | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | CONSTRUCTIVE HEURISTICS | Greedy Heuristics for Allocation(HW/SW Mapping) and scheduling | ALLOCATION, SCHEDULING |
Deployment, Scheduling, Resposnbility mapping (task to module) | SIMPLE EXAMPLE | Illustrative Example | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | Compare the experimental results with related approaches | |||||
| Koziolek11R | Towards A Generic Quality Optimisation Framework for Component Based System Models | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | PERFORMANCE, RELIABILITY |
NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | ALLOCATION, HARDWARE SELECTION, SOFTWARE SELECTION |
NOT PRESENTED | NOT PRESENTED | |||||||||||||||
| LeBeux10BNBLP | Combining mapping and partitioning exploration for NoC-based embedded systems | EMBEDDED SYSTEMS | system on chip | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB, AF;Throughput is MB, area and flexibility SAF | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | NSGA-II | ALLOCATION, OTHER PROBLEM SPECIFIC |
They call it mapping (Deployment) and partitioning (whether a task is implemented in hardware or software = Other problem specific) | ACADEMIC CASE STUDY | GSM voice encoder application, looks quite realistic | NOT PRESENTED | artifiical experiments to analyse scalability, e.g. how well it handles large problem instances. Or is that validation of approach? | ||||||||||
| Li09CE | SLA-driven Planning and Optimization of Enterprise Applications | INFORMATION SYSTEMS | Optimisation of a SAP system configuration | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | Or rather configuration time | COST, PERFORMANCE |
Response time, Cost;Cost in terms of Hardware total Cost of owbership, based on IBM processor pricing and power considerations | NOT PRESENTED, NOT PRESENTED |
all SLA constraints are mapped to objectives in the MOA, |
NOT PRESENTED | MODEL BASED | MB;Finite capacity queueing Model for Performance, regression-based Cost Model | NONLINEAR MIXED INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Name of used algorithm: SMS-EMOA | HARDWARE PARAMETERS, SOFTWARE PARAMETERS |
Resource speed, number of threads, number of cores | INDUSTRIAL CASE STUDY | real world, SAP system (though quite abstractely Modelled | NOT PRESENTED | Not required as they just use the EMO out of the box | ||||||
| Limbourg08K | Multi-objective optimization of Generalized Reliability design problems using feature Models—A concept for early design stages | GENERAL | Muliti-objective optimization of systems design, in General | MULTI-OBJECTIVE OPTIMIZATION | probabilistic design goals | DESIGN-TIME | Early design decisions | COST, RELIABILITY |
Reliability,Cost;Present a General approach. This paper contains only Reliability, Cost | GENERAL, PROHIBIT |
Multiple->GENERAL. Multiple constraints are embedded in to feature Models. Design alternatives are generated from featuer Models., Since the solutions are generated from feature Models, constraints are automatically satisfied? |
PROHIBIT | Since the solutions are generated from feature Models, constraints are automatically satisfied? | MODEL BASED | MB;Reliability block diagrams | NONLINEAR INTEGER | allocating Redundancy level | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Multi-objective Evolutionary algorithm | COMPONENT SELECTION, HARDWARE REPLICATION |
RAP with non-identical redundant Components | SIMPLE EXAMPLE | RAP example | NOT PRESENTED | ||||
| Marseguerra04ZP | A multiobjective genetic algorithm approach to the optimization of the technical specifications of a nuclear safety system | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST |
Cost, Unavilability; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Markov Model, with uncertainty, Monte Carlo Simulation | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA | MAINTENANCE SCHEDULES | Surveillance Test Intervals (STI) and allowable bypass time (ABT), the latter is how long the system is allowed to run without a component being active, so I would see this as a maintenance schedule aspect, too. | INDUSTRIAL CASE STUDY | Reactor Protection System | NOT PRESENTED | ||||||||
| Marseguerra05ZP | Multiobjective spare part Allocation by means of genetic algorithms and Monte Carlo simulation | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST |
Lifecycle Cost (Unavilability, Purchase, Maintance Cost), Volume; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Markov Model, with uncertainty, Monte Carlo Simulation | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA | HARDWARE REPLICATION | "the number of spare parts to be kept in storage for each component type." | INDUSTRIAL CASE STUDY | Reactor Protection System | NOT PRESENTED | ||||||||
| Marseguerra07ZP | Genetic Algorithms and Monte Carlo Simulation for the Optimization of System Design and Operation | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | AVAILABILITY, COST |
Cost, Unavilability; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Markov Model, with uncertainty, Monte Carlo Simulation | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, COMPONENT SELECTION |
p. 119, choice of redundancy configuration. As they do not mention software at all, this is most likely hardware, p. 119, choice of components |
INDUSTRIAL CASE STUDY | Reactor Protection Instrumentation System (RPIS) of a Pressurized Water Reactor (PWR) | NOT PRESENTED | |||||||||
| Martens10AKM | A Hybrid Approach for Multi-attribute QoS Optimisation in Component Based Software Systems | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE, RELIABILITY |
Performance, Reliability, Cost;Response time, POFOD, simple Cost Model (Component Cost and Hardware Cost) | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Palladio Component Model | NONLINEAR MIXED INTEGER | Extended queueing network, simulation or approximation required for evaluation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | hybrid: start population determined by MILP solution | COMPONENT SELECTION, ALLOCATION, HARDWARE SELECTION, HARDWARE PARAMETERS |
Component Selection, Component Allocation, number of servers, processing speed | ACADEMIC CASE STUDY | Small artificial Case Study | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | comparison with pure Evolutionary algorithm | ||||||||
| Martens10KBR | Automatically Improve Software Architecture Models for Performance, Reliability, and Cost Using Evolutionary Algorithms | INFORMATION SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, PERFORMANCE, RELIABILITY |
Performance, Reliability, Cost;Response time, POFOD, simple Cost Model (Component Cost and Hardware Cost) | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Palladio Component Model | NONLINEAR MIXED INTEGER | Extended queueing network, simulation or approximation required for evaluation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Manually Added. | COMPONENT SELECTION, ALLOCATION, HARDWARE SELECTION, HARDWARE PARAMETERS |
Component Selection, Component Allocation, number of servers, processing speed | ACADEMIC CASE STUDY | Small artificial Case Study | COMPARISON WITH RANDOM SEARCH | comparison with random search | ||||||||
| Meedeniya10BAG | Architecture-Driven Reliability and Energy Optimization for Complex Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY, ENERGY |
Reliability, Energy; | REDUNDANCY LEVEL, PROHIBIT |
PROHIBIT | MODEL BASED | MB; | NONLINEAR INTEGER | Redundancy allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED | ||||||||||||
| Menasce07D | Utility-based QoS Brokering in Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | PERFORMANCE | Response Time, Throughput;Use of Utility Functions | QOS VALUES, PROHIBIT |
exclusion, |
PROHIBIT | MODEL BASED | MB;Queuing networks (LQN) | NONLINEAR INTEGER | EXACT | EXACT STANDARD | EXHAUSTIVE SEARCH | Brute Force | SERVICE SELECTION | ACADEMIC CASE STUDY | Travel Planer Case Study | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Menasce07RG | QoS management in service-oriented architectures | INFORMATION SYSTEMS | GENERAL | RUN-TIME | PERFORMANCE, RELIABILITY |
Performance, Fault Tolerance; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Queuing networks (LQN) | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | OTHER PROBLEM SPECIFIC | AGENT NEGOTIATION | SERVICE SELECTION | EXPERIMENTS | Experiments with generated Example | NOT PRESENTED | |||||||||||
| Menasce08CD | A Heuristics Approach to Optimal Service Selection in Service Oriented Architectures | INFORMATION SYSTEMS | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | RUN-TIME | COST, PERFORMANCE |
Response Time, Cost; | PERFORMANCE, COST, PROHIBIT |
exclusion IM:ResponseTime->Performance, exclusion, |
PROHIBIT | MODEL BASED | MB;Queuing networks (LQN) | NONLINEAR INTEGER | APPROXIMATIVE | PROBLEM-SPECIFIC HEURISTIC | RESTRICTED ENUMERATION OF ALL POSSIBLE SOLUTIONS | Jensen-based Optimal Service Selection | SERVICE SELECTION | ACADEMIC CASE STUDY | Abstract Case Study | NOT PRESENTED | ||||||||||
| Nicholson97B | Emergence of an Architectural Topology for Safety-Critical Real-Time Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | COST, RELIABILITY, PERFORMANCE |
Reliability,Cost, topology size;Attributes are converted to single Function using Weighted sum | GENERAL, PROHIBIT |
Not specifically mentioned, |
PROHIBIT | MODEL BASED | MB;Attributes are converted to single Function using Weighted sum | NONLINEAR INTEGER | topology Selection. | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Genetic algorithm | HARDWARE SELECTION, COMPONENT SELECTION, HARDWARE REPLICATION, SOFTWARE REPLICATION, ALLOCATION |
SIMPLE EXAMPLE | Example in Integrated Modular Avionics | NOT PRESENTED | |||||||||
| Ortmeier04R | Safety Optimization: A combination of fault tree analysis and optimization techniques | EMBEDDED SYSTEMS | Safety Critical System | MULTIPLE OBJECTIVES TRANSFORMED TO SINGLE | DESIGN-TIME | COST, SAFETY |
Cost, Safety; | DESIGN, PROHIBIT |
PROHIBIT | MODEL BASED | MB;Fault Tree | NONLINEAR MIXED INTEGER | GENERAL | GENERAL | GENERAL | They name examples such as simple methods (linear programming) | SOFTWARE PARAMETERS, MAINTENANCE SCHEDULES |
Desing Parameter, average maintenance interval: "the tolerance of a speed indicator, accepted time delay between request and answers or the average maintenance interval are all free parameters of different systems." (p. 2), Desing Parameter, average maintenance interval |
INDUSTRIAL CASE STUDY | height control system of the elbtunnel | NOT PRESENTED | |||||||||
| Papadopoulos04G | Evolving car designs using model-based automated safety analysis and optimisation techniques | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | SAFETY | Safety, Cost; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Fault Trees, FMEA tables | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA similar | COMPONENT SELECTION, OTHER PROBLEM SPECIFIC |
, which function to implement p. 81 |
INDUSTRIAL CASE STUDY | Brake by wire | NOT PRESENTED | ||||||||
| Pattison99A | Genetic Algorithms in Optimal Safety Design | EMBEDDED SYSTEMS | Safety Critical System | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | SAFETY | Safety (system unAvailability); | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Fault trees are constructed and then efficiently evaluated with BDDs | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Simple GA SGA | HARDWARE REPLICATION, COMPONENT SELECTION, MAINTENANCE SCHEDULES, SOFTWARE REPLICATION |
replication: duplicates element (p.1), , diversity, i.e. several ways to achieve the same mean (p.1) Does not fit perfectly for software replication, but almost |
ACADEMIC CASE STUDY | High-integrity protection system | NOT PRESENTED | |||||||||
| Qiu00WP | Dynamic Power Management of Complex Systems Using Generalized Stochastic Petri Nets | EMBEDDED SYSTEMS | SINGLE-OBJECTIVE OPTIMIZATION | RUN-TIME | ENERGY | Energy;EC | GENERAL, PROHIBIT |
Performance (Delay), Concurrency, mutual exclusion, conflict, |
PROHIBIT | MODEL BASED | MB;GSPN -> converted to CT-MDPs | NONLINEAR MIXED INTEGER | time and ploicy are changed | EXACT | EXACT STANDARD | LINEAR PROGRAMMING | SOFTWARE PARAMETERS | "- use the Models GSPN with Cost and controllable GSPN with Cost - Metrics for optimization : power consumption and Performance (Delay)" | NOT NEEDED, NOT PRESENTED, NOT PRESENTED |
, Manually Added |
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| Ren98D | Design of Reliable Systems Using Static & Dynamic Fault Trees | EMBEDDED SYSTEMS | Embedded system design | MULTI-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, WEIGHT, PHYSICAL, PENALTY |
, Physical size, Fitness Function includes constraints, resulting a penalty effect |
PENALTY | Fitness Function includes constraints, resulting a penalty effect | MODEL BASED | MB;Fault trees, BDDs are used to quantify Reliability | NONLINEAR INTEGER | Component Selection and Redundancy Allocation | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | HARDWARE SELECTION, HARDWARE REPLICATION |
INDUSTRIAL CASE STUDY | Case Study of Cardiac-assist system design | NOT PRESENTED | ||||||||
| Riauke07B | An offshore safety system optimization using an SPEA2-based approach | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | SAFETY | Safety (system unAvailability), Lifecycle Cost; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED | MB;Fault Tree | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | SPEA | HARDWARE REPLICATION, HARDWARE SELECTION, MAINTENANCE SCHEDULES, HARDWARE PARAMETERS |
e.g. number of pressure transmitters, (p.8), e.g. pressure transmitter type, e.g. maintenance test intervals for the firewater pump, e.g. percentage capacity of firewater pumps (p.8) |
ACADEMIC CASE STUDY | firewater deluge system | NOT PRESENTED | ||||||||
| Shankaran06BSBLMD | A Framework for (Re)Deploying Components in Distributed Real-time and Embedded Systems | EMBEDDED SYSTEMS | MULTI-OBJECTIVE OPTIMIZATION | RUN-TIME | GENERAL | General;Supports Multiple attributes | NOT PRESENTED, NOT PRESENTED |
Not Presented, |
NOT PRESENTED | MODEL BASED | MB;Models Not Presented | NONLINEAR INTEGER | Deployment problem | GENERAL | GENERAL | GENERAL | Supports Multiple algorithms | ALLOCATION | The paper presents a framework in very brief, lots of details are missing | NOT PRESENTED | Mention about a naval shipboard computer system and NASA earth science mission | NOT PRESENTED | ||||||||
| Torres-Echeverria08MT | Design optimization of a safety-instrumented system based on RAMS+ C addressing IEC 61508 requirements and diverse Redundancy | EMBEDDED SYSTEMS | Safety Critical System | MULTI-OBJECTIVE OPTIMIZATION | Pareto optimal solution | DESIGN-TIME | SAFETY | Safety (system unAvailability), Lifecycle Cost; | NOT PRESENTED, NOT PRESENTED |
NOT PRESENTED | MODEL BASED, SIMPLE AGGREGATION FUNCTIONS |
MB, AF;Fault Tree, Cost sums | NONLINEAR INTEGER | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | MOGA | SOFTWARE REPLICATION, HARDWARE REPLICATION |
Redundancy Allocation (Diverse Components) p.1, p.1 |
ACADEMIC CASE STUDY | applied to the design of a chemical reactor’s protection system against high pressure and temperature | NOT PRESENTED | ||||||||
| Wadekar99G | Exploring Cost and Reliability Tradeoffs in Architectural Alternatives using a Genetic Algorithm | GENERAL | Software architecture optimization in General | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, PENALTY |
, Constraint is considered in fitness Function, resulting a penalty efferct |
PENALTY | Constraint is considered in fitness Function, resulting a penalty efferct | MODEL BASED | MB;DTMC based approach | NONLINEAR INTEGER | Component Selection and selecting of Reliability increment options | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | COMPONENT SELECTION | INDUSTRIAL CASE STUDY | 3 Case Study are presetned | NOT PRESENTED | no proper validation of the optimization as I see | |||||||
| Yeh10H | Solving reliability redundancy allocation problems using an artificial bee colony algorithm | GENERAL | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability; | COST, WEIGHT, VOLUME, PROHIBIT |
PROHIBIT | MODEL BASED | MB; | NONLINEAR MIXED INTEGER | Redundancy allocation | APPROXIMATIVE | METAHEURISTIC | ARTIFICIAL BEE COLONY ALGORITHM | SOFTWARE REPLICATION | SIMPLE EXAMPLE | COMPARISON WITH BASELINE HEURISTIC ALGORITHM | comparison with other optimisation algorithms | |||||||||||
| Zhao04L | Redundancy optimization problems with uncertainty of combining randomness and fuzziness | EMBEDDED SYSTEMS | no domain named, but seems to fit more to Hardware Components / ES | SINGLE-OBJECTIVE OPTIMIZATION | DESIGN-TIME | RELIABILITY | Reliability;different variants are discussed: mission time Reliability, expected system lifetime, system lifetime according to given confidence levels | COST, PROHIBIT |
Cost = sum of subsystem Cost, Not clear, but seems to be Prohibited |
PROHIBIT | Not clear, but seems to be Prohibited | MODEL BASED | random fuzzy simulation, neural network;Component lifetimes are random fuzzy variables; thus, quality canNot simply be evaluated through AF | NONLINEAR CONTINOUS | decesion variables are fuzzy sets, can take non-Integer values | APPROXIMATIVE | METAHEURISTIC | EVOLUTIONARY ALGORITHM | Hybrid intelligent algorithm: before the main algorithm starts, first a neural network is trained for efficient quality evaluation of candidates | HARDWARE REPLICATION, SOFTWARE REPLICATION |
SIMPLE EXAMPLE | NOT PRESENTED |
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