Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
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
Performance Measures for Dynamic Environments
PPSN VII Proceedings of the 7th 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
Population-based incremental learning with memory scheme for changing environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop 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
Proceedings of the 38th conference on Winter simulation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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
Benchmarks for dynamic multi-objective optimisation algorithms
ACM Computing Surveys (CSUR)
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This paper proposes a new parallel evolutionary procedure to solve multi-objective dynamic optimization problems along with some measures to evaluate multi-objective optimization in dynamic environments. These dynamic optimization problems appear in quite different real-world applications with actual socio-economic relevance. In these applications, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online whilst the size of the changes is unknown. Although parallel processing could be very useful in these problems to meet the solution quality requirements and constraints, to date, not many parallel approaches have been reported in the literature. Taking this into account, we introduce a multi-objective optimization procedure for dynamic problems that are based on PSFGA, a parallel evolutionary algorithm previously proposed by us for multi-objective optimization. It uses an island model where a process divides the population among the remaining processes and allows the communication and coordination among the subpopulations in the different islands. The proposed algorithm makes an exclusive use of non-dominating individuals for the selection and variation operator and applies a crowding mechanism to maintain the diversity and the distribution of the solutions in the Pareto front. We also propose a model to understand the benefits of parallel processing in multi-objective problems and the speedup figures obtained in our experiments.