期刊
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
卷 50, 期 12, 页码 5338-5350出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2018.2875043
关键词
Sociology; Statistics; Optimization; Convergence; Cybernetics; Evolutionary computation; Benchmark testing; Biased self-adaptive mutation selection (BiasMOSaDE); differential evolution (DE); multiobjective optimization (MOP); personal archive
资金
- National Key Research and Development Program of China [2016YFB0901900]
- Major Program of National Natural Science Foundation of China [71790614]
- Fund for the National Natural Science Foundation of China [61374203, 61573086]
- Fund for Innovative Research Groups of the National Natural Science Foundation of China [71621061]
- Major International Joint Research Project of the National Natural Science Foundation of China [71520107004]
- 111 Project [B16009]
Differential evolution is one of the most powerful evolutionary algorithms for single objective optimization problems in the literature. Its application in the multiobjective optimization problems is also very successful, and many kinds of promising multiobjective differential evolution (MODE) algorithms have been proposed in the literature. This paper develops a new variant of MODE with two features. First, a set of personal archives are maintained to evolve the search process instead of a population with a fixed size, and a truncation procedure is used to enhance selection pressure. Second, multiple biased mutation operators incorporating the target solution quality are proposed, and an adaptive selection method is adopted to allocate the mutation operators to solutions. The proposed MODE is referred to as the MODE with personal archive and biased self-adaptive mutation selection (BiasMOSaDE). A set of 31 benchmark multiobjective problems selected from the literature are adopted to evaluate its performance. Computational results illustrate that the proposed BiasMOSaDE is competitive or even superior to several state-of-the-art MODEs in the literature.
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