The cooperative coevolution algorithm performs well in solving large-scale global optimization problems.The core idea of cooperative coevolution is to utilize a divide-and-conquer strategy for decomposing high-dimensional problems into multiple subproblems,which are then processed individually and separately.However,existing decomposi-tion methods typically require significant computational cost to obtain accurate variable grouping.To address this issue,a novel three-level recursive differential grouping strategy(NTRDG)is proposed in this paper,which simplifies the group-ing process by utilizing historical information in recursive interaction detection and avoids the detection of relationships among certain sets,leading to a lower computational cost without sacrificing grouping accuracy.Simulation results demon-strate that compared to four existing methods,NTRDG exhibits a stronger competitiveness in solving large-scale global optimization problems.
关键词
全局优化/协同进化/分解方法/三层递归差分分组/递归搜索
Key words
global optimization/cooperative coevolution/decomposition methods/three-level recursive differential grouping/recursive search
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