Stochastic Opposition-Based Learning Using a Beta Distribution in Differential Evolution

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Since it first appeared, differential evolution (DE), one of the most successful evolutionary algorithms, has been studied by many researchers. Theoretical and empirical studies of the parameters and strategies have been conducted, and numerous variants have been proposed. Opposition-based DE (ODE), one of such variants, combines DE with opposition-based learning (OBL) to obtain a high-quality solution with low-computational effort. In this paper, we propose a novel OBL using a beta distribution with partial dimensional change and selection switching and combine it with DE to enhance the convergence speed and searchability. Our proposed algorithm is tested on various test functions and compared with standard DE and other ODE variants. The results indicate that the proposed algorithm outperforms the comparison group, especially in terms of solution accuracy
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2016-10
Language
English
Article Type
Article
Keywords

ADAPTING CONTROL PARAMETERS; MULTIOBJECTIVE OPTIMIZATION; GENETIC ALGORITHMS; MUTATION; STRATEGIES; BEHAVIOR

Citation

IEEE TRANSACTIONS ON CYBERNETICS, v.46, no.10, pp.2184 - 2194

ISSN
2168-2267
DOI
10.1109/TCYB.2015.2469722
URI
http://hdl.handle.net/10203/214001
Appears in Collection
EE-Journal Papers(저널논문)
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