期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 12, 页码 2848-2861出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2490669
关键词
Archive search; cooperative populations; differential evolution (DE); many-objective optimization; multiobjective optimization
类别
资金
- National High-Technology Research and Development Program (863 Program) of China [2013AA01A212]
- National Natural Science Foundation of China for Distinguished Young Scholars [61125205]
- National Natural Science Foundation of China [61332002, 61300044]
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.
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