A Multi-objective Differential Evolution Algorithm with Elite-archive and Opposition-based Learning

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A Multi-objective Differential Evolution Algorithm with Elite-archive and Opposition-based Learning

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[1]WANG Shenwen,WANG Jiaying,ZHANG Jiaxing,et al.A Multi-objective Differential Evolution Algorithm with Elite-archive and Opposition-based Learning[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):40-45.[doi:10.13705/j.issn.1671-6833.2020.06.011]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 41卷 Number of periods: 2020 06 Page number: 40-45 Column: Public date: 2020-12-31

Title:: A Multi-objective Differential Evolution Algorithm with Elite-archive and Opposition-based Learning

Author(s):: WANG Shenwen1; 2; WANG Jiaying1; 2; ZHANG Jiaxing1; 2; WANG Feng3; WANG Hui4; 1.School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China; 2.Laboratory of Artificial Intelligence and Machine Learning, Hebei GEO University, Shijiazhuang 050031, China; 3.School of Computer Science, Wuhan University, Wuhan 430072, China; 4.School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099,China

Keywords:: multi-objective optimization; elite-archive; opposition-based learning; differential evolution algorithm; constrained decomposition with grids

CLC:: -

DOI:: 10.13705/j.issn.1671-6833.2020.06.011

Abstract:: The multi-objective optimization problem became more and more complex. Inspired by the ensemble algorithm, a multi-objective differential evolution algorithm with elite-archive and opposition-based learning is proposed in this paper. In this algorithm, an external archive was created to save the nondominated solutions in the evolutionary process of the population. It used the preset opposition-based generation jumping andopposition-based learning to generate the different solutions of the individual and stored in the elite archive, to expand the search scope and improve population diversity. The grid was used to determines the coordinates of the solutions, and the restricted mating pool was generated according to certain constraints. The parent solutions were selected in the restricted mating pool to produce the new individual by using differential evolution algorithm, then generated the next iteration population by constrained decomposition with grids sorting. The experimental results showed that the proposed algorithm had strong robustness with the shapes of PFs in solving unconstrained multi-objective optimization problems was superior to some state-of-the-art multi-objective algorithms in diversity and convergence on UF test problems.

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