计算机应用研究2026，Vol.43Issue(4)：1098-1111,14.DOI:10.19734/j.issn.1001-3695.2025.08.0304
融合聚类线性组合与优化状态自适应的差分进化算法
Differential evolution algorithm based on clustering linear combination and optimization state adaptation
摘要
Abstract
To address the issues of the DE algorithm,such as high parameter sensitivity,insufficient global exploration capa-bility,and imbalance between exploration and exploitation processes in high-dimensional complex function optimization,this paper proposed an improved algorithm named clustering linear combination and optimization state adaptive differential evolution(CLOSADE),which integrated a clustering linear combination approach with an optimization state adaptive mechanism.The research aimed to enhance the algorithm's robustness and convergence performance when handling complex optimization prob-lems.This method firstly designed a clustering strategy based on dual factors of fitness and distance to generate multiple clus-ters of linear combination vectors and introduced a dynamic distance threshold to enhance population diversity.Secondly,it constructed an indicator of optimization state(IOS)to quantify population distribution characteristics,driving the adaptive ad-justment of mutation strategies and control parameters.Experimental results demonstrate that,on the CEC2017 and CEC2022 benchmark test functions,CLOSADE significantly outperforms advanced algorithms such as JSO,NL-SHADE-DP,and S-SHADE-DP in terms of both convergence accuracy and speed.Particularly on high-dimensional hybrid and composite func-tions,CLOSADE exhibits remarkable advantages,with an average improvement of 22％in convergence accuracy and approxi-mately 40％in convergence speed.Further population diversity analysis reveals that the multi-subgroup structure formed through clustering effectively maintains parallel search capabilities in the solution space,while the optimization state indicator ensures a dynamic balance between exploration and exploitation behaviors at different evolutionary stages of the algorithm.关键词
差分进化/聚类线性组合/状态自适应/参数自适应Key words
differential evolution(DE)/clustering linear combination/state adaptation/parameter adaptation分类
信息技术与安全科学引用本文复制引用
熊才权,李昊,閤大海,吴歆韵,罗茂..融合聚类线性组合与优化状态自适应的差分进化算法[J].计算机应用研究,2026,43(4):1098-1111,14.基金项目
国家自然科学基金资助项目(62402164) （62402164）
