An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
PSFGA: parallel processing and evolutionary computation for multiobjective optimisation
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Optimal voltage allocation techniques for dynamically variable voltage processors
ACM Transactions on Embedded Computing Systems (TECS)
Population-based incremental learning with memory scheme for changing environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Application of multi-objective simulation-optimization techniques to inventory management problems
WSC '05 Proceedings of the 37th conference on Winter simulation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Proceedings of the 38th conference on Winter simulation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A fast and effective method for pruning of non-dominated solutions in many-objective problems
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Dynamic multiobjective optimization problems: test cases, approximations, and applications
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
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In this paper a generic parallel procedure for dynamic problems using evolutionary algorithms is presented. In dynamic multi-objective problems, the objective functions, the constraints and hence, also the solutions, can change over time and usually demand to be solved online. Thus, high performance computing approaches, such as parallel processing, should be applied to these problems to meet the solution constraints and quality requirements. Taking this into account, we introduce a generic parallel procedure for multi-objective evolutionary algorithms, through a master-slave paradigm. This generic parallel procedure is used to compare the parallel processing of a few multi-objective optimisation evolutionary algorithms: our proposed algorithms, SFGA and SFGA2, in conjunction with SPEA2 and NSGA-II. We also give a model to understand the benefits of parallel processing in dynamic multi-objective problems and the speedup results observed in our experiments.