Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A New Model of Parallel Distributed Genetic Algorithms for Cluster Systems: Dual Individual DGAs
ISHPC '00 Proceedings of the Third International Symposium on High Performance Computing
Various island-based parallel genetic algorithms for the 2-page drawing problem
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
A Hierarchical Approach in Distributed Evolutionary Algorithms for Multiobjective Optimization
Large-Scale Scientific Computing
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
A study of the parallelization of the multi-objective metaheuristic MOEA/D
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
PSFGA: a parallel genetic algorithm for multiobjective optimization
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Parallelization of multi-objective evolutionary algorithms using clustering algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Journal of Intelligent Manufacturing
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Using multiple local evolutionary searches, instead of single and overall search, has been an effective technique to solve multi-objective optimization problems (MOPs). With this technique, many parallel and distributed multi-objective evolutionary algorithms (dMOEAs) on different island models have been proposed to search for optimal solutions, efficiently and effectively. These algorithms often use local MOEAs on their islands in which each local search is considered to find a part of optimal solutions. The islands (and the local MOEAs), however, need to communicate to each other to preclude the possibility of converging to local optimal solutions. The existing dMOEAs rely on the central and iterative process of subdividing a large-scale population into multiple subpopulations; and it negatively affects the dMOEAs performance. In this paper, a new version of dMOEA with new local MOEAs and migration strategy is proposed. The respective objective space is first subdivided into the predefined number of polar-based regions assigned to the local MOEAs to be explored and exploited. In addition, the central and iterative process is eliminated using a new proposed migration strategy. The algorithms are tested on the standard bi-objective optimization test cases of ZDTs, and the result shows that these new dMOEAs outperform the existing distributed and parallel MOEAs in most cases.