Cooperative Differential Evolution With Multiple Populations for Multiobjective Optimization

This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subp...

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Veröffentlicht in:IEEE transactions on cybernetics Jg. 46; H. 12; S. 2848 - 2861
Hauptverfasser: Wang, Jiahai, Zhang, Weiwei, Zhang, Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.12.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Approximation methods
Archive search
Archives & records
Computer simulation
cooperative populations
Customer relationship management
differential evolution (DE)
Estimation
Evolutionary algorithms
Evolutionary computation
Frequency modulation
many-objective optimization
Mathematical programming
multiobjective optimization
Multiple objective analysis
Optimization
Parameter sensitivity
Pareto optimization
Populations
Sociology
Statistics
ISSN:2168-2267, 2168-2275, 2168-2275
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Zusammenfassung:This paper presents a cooperative differential evolution (DE) with multiple populations for multiobjective optimization. The proposed algorithm has M single-objective optimization subpopulations and an archive population for an M-objective optimization problem. An adaptive DE is applied to each subpopulation to optimize the corresponding objective of the multiobjective optimization problem (MOP). The archive population is also optimized by an adaptive DE. The archive population is used not only to maintain all nondominated solutions found so far but also to guide each subpopulation to search along the whole Pareto front. These (M + 1) populations cooperate to optimize all objectives of the MOP by using adaptive DEs. Simulation results on benchmark problems with two, three, and many objectives show that the proposed algorithm is better than some state-of-the-art multiobjective DE algorithms and other popular multiobjective evolutionary algorithms. The online search behavior and parameter sensitivity of the proposed algorithm are also investigated.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2015.2490669