关键词: 大规模重叠问题, 差分分组, 协同进化, 计算资源消耗, 进化算法

Abstract:

Large-scale overlapping problems are prevalent in practical engineering applications， and the optimization challenge is significantly amplified due to the existence of shared variables. Decomposition-based Cooperative Co-evolution （CC） algorithms have demonstrated promising performance in addressing large-scale overlapping problems. However， certain novel CC frameworks designed for overlapping problems rely on grouping methods for the identification of overlapping problem structures and the current grouping methods for large-scale overlapping problems fail to consider both accuracy and efficiency simultaneously. To address the above problems， a Two-Stage Differential Grouping （TSDG） method for large-scale overlapping problems was proposed， which achieves accurate grouping while significantly reducing computational resource consumption. In the first stage， a grouping method based on the finite difference principle was employed to efficiently identify all subcomponents and shared variables. To enhance both stability and accuracy in grouping， a grouping refinement method was proposed in the second stage to examine the information of the subcomponents and shared variables obtained in the previous stage and correct inaccurate grouping results. Based on the synergy of the two stages， TSDG achieves efficient and accurate decomposition of large-scale overlapping problems. Extensive experimental results demonstrate that TSDG is capable of accurately grouping large-scale overlapping problems while consuming fewer computational resources. In the optimization experiment， TSDG exhibits superior performance compared to state-of-the-art algorithms for large-scale overlapping problems.

Key words: large-scale overlapping problem, differential grouping, Cooperative Co-evolution (CC), computational resource consumption, evolutionary algorithm