期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 52, 期 6, 页码 4246-4259出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3025577
关键词
Cooperative co-evolution (CC); contribution-based optimization (CBO); evolution strategy; large-scale global optimization (LSGO); overlapping problem
类别
资金
- Marsden Fund of New Zealand Government [VUW1509, VUW1614]
- Science for Technological Innovation Challenge (SfTI) Fund [E3603/2903]
- MBIE SSIF Fund [VUW RTVU1914]
This study proposes a new contribution-based cooperative co-evolutionary algorithm to address non-separable large-scale problems with overlapping subcomponents. The algorithm outperforms existing methods due to its novel decomposition method and optimization framework.
Cooperative co-evolutionary algorithms have addressed many large-scale problems successfully, but the non-separable large-scale problems with overlapping subcomponents are still a serious difficulty that has not been conquered yet. First, the existence of shared variables makes the problem hard to be decomposed. Second, existing cooperative co-evolutionary frameworks usually cannot maintain the two crucial factors: high cooperation frequency and effective computing resource allocation, simultaneously when optimizing the overlapping subcomponents. Aiming at these two issues, this article proposes a new contribution-based cooperative co-evolutionary algorithm to decompose and optimize nonseparable large-scale problems with overlapping subcomponents effectively and efficiently: 1) a contribution-based decomposition method is proposed to assign the shared variables. Among all the subcomponents containing a shared variable, the one that contributes the most to the entire problem will include the shared variable and 2) to achieve the two crucial factors at the same time, a new contribution-based optimization framework is designed to award the important subcomponents based on the round-robin structure. Experimental studies show that the proposed algorithm performs significantly better than the state-of-the-art algorithms due to the effective grouping structure generated by the proposed decomposition method and the fast optimizing speed provided by the new optimization framework.
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