CCFR3: A cooperative co-evolution with efficient resource allocation for large-scale global optimization. (1st October 2022) - Yang, Ming; Zhou, Aimin; Lu, Xiaofen; Cai, Zhihua; Li, Changhe; Guan, Jing
Abstract: Cooperative co-evolution (CC) adopts the divide-and-conquer strategy to decompose an optimization problem, which can decrease the difficulty of solving large-scale optimization problems. Each decomposed subproblem is solved by a subpopulation. According to the contributions of the subpopulations to the improvement of the best overall objective value, the CC algorithms select the subpopulation with the greatest contribution to undergo evolution. In the existing CC algorithms, the contribution evaluation cannot adapt to solve the optimization problem, which may decrease the performance of CC. In this paper, we propose a new CC framework named CCFR3, which can adaptively evaluate the contribution of a subpopulation in each co-evolutionary cycle. CCFR3 can allocate computational resources among subpopulations more frequently than other contribution-based CC algorithms. The subpopulations can have more chances to undergo evolution, which is beneficial to speed up the convergence of CC and enhance the performance of CC on obtaining the global optimal solution. Our experimental results and analysis suggest that CCFR3 is a competitive solver for large-scale optimization problems. Highlights: Contribution-based cooperative co-evolution. Self-adaptive evaluation on contributions. Efficient computational resource allocation. Competitive performance on solving partially separable problems.