Parameter and strategy adaptive differential evolution algorithm based on accompanying evolution

•The adaptation of parameter and strategy is realized through accompanying evolution.•Accompanying population and generalized opposition-based learning enhance the population diversity.•Radial spatial projection is used for dynamic analysis of evolutionary direction. Differential evolution (DE) is a...

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Bibliographic Details
Published in:Information sciences Vol. 607; pp. 1136 - 1157
Main Authors: Wang, Minghao, Ma, Yongjie, Wang, Peidi
Format: Journal Article
Language:English
Published: Elsevier Inc 01.08.2022
Subjects:
Accompanying evolution
Differential evolution
Generalized opposition-based learning
Parameter adaptation
Radial space projection
Strategy adaptation
Differential evolution
Generalized opposition-based learning
Radial space projection
Accompanying evolution
Parameter adaptation
Strategy adaptation
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:•The adaptation of parameter and strategy is realized through accompanying evolution.•Accompanying population and generalized opposition-based learning enhance the population diversity.•Radial spatial projection is used for dynamic analysis of evolutionary direction. Differential evolution (DE) is an intelligent optimization algorithm inspired by biological evolution. Setting a mutation strategy and control parameters that meet the optimization requirements are the premise for DE to achieve good performance. This paper proposes a parameter and strategy adaptive differential evolution algorithm based on accompanying evolution (APSDE). Through the accompanying population, in which individuals are composed of suboptimal solutions, the mutation strategy and control parameters are optimized to realize the adaptation of the strategy and parameters of the main population. Population diversity is enhanced in evolution by generating reverse individuals. In addition, radial spatial projection technology is utilized to track the change in evolution direction with optimization. The performance of APSDE is validated under four sets of benchmark problem suites from the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC), and compared with state-of-the-art optimization algorithms. The results show that the proposed algorithm has better optimization performance than the competitive algorithms because of its efficient adaptive mechanism and its excellent population diversity.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2022.06.040