Particle swarm optimization based on K-means clustering and adaptive dual-groups strategy

doi:10.1016/j.swevo.2025.102226

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	Particle swarm optimization based on K-means clustering and adaptive dual-groups strategy
	Fan, Yuyu 1; Tian, Dongping 1; Xu, Qinghao 1; Sun, Jie 2; Xu, Qiu 3; Shi, Zhongzhi 4
	2026
发表期刊	SWARM AND EVOLUTIONARY COMPUTATION
ISSN	2210-6502
卷号	100 页码:45
摘要	Particle swarm optimization (PSO) is a swarm intelligence algorithm that simulates the cooperative foraging behavior of bird flocks and searches for the optimal solution by iterating and updating the position and speed of particles. Its advantages are that the principle is simple and easy to implement, the convergence speed is fast, and it is suitable for high-dimensional problems. Nevertheless, it has the drawbacks of being prone to fall into local optimum, having low search efficiency in the later stage and relying on experience for parameter setting. Hence, this paper puts forward a particle swarm optimization algorithm based on k-means clustering and adaptive dualgroups strategy (PSO-KCAD) to solve the related problems mentioned above. First, a twin swarm collaborative search strategy is employed to co-evolve collaboratively and balance the exploration in the early stage of the search and the exploitation in the later stage. Second, comprehensive learning and subgroup elite-ordinary particle stratification strategy are used to promote communication among particles and thereby accelerate the convergence process. Subsequently, the adaptive probability-driven elite replacement and competitive disturbance mechanism are utilized to maintain population diversity and improve the accuracy of solutions. Finally, the performance of PSO-KCAD is compared with that of several other PSO variants on CEC2017. The experimental results show that PSO-KCAD is markedly superior to other algorithms. To further verify the effectiveness and robustness of our proposal, we apply it to two real-world problems and the results show that it has also achieved the most promising optimization results.
关键词	Particle swarm optimization Twin swarm collaboration Comprehensive learning,Probability-driven elite replacement,Competitive disturbance mechanism
DOI	10.1016/j.swevo.2025.102226
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号	WOS:001626836100002
出版者	ELSEVIER
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/43086
专题	中国科学院计算技术研究所
通讯作者	Tian, Dongping
作者单位	1.Baoji Univ Arts & Sci, Inst Comp Software, Baoji 721007, Shaanxi, Peoples R China 2.Changji Univ, Sch Informat Engn, Changji 831100, Xinjiang, Peoples R China 3.King Abdullah Univ Sci & Technol, Phys Sci & Engn Div, Thuwal 23955, Saudi Arabia 4.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Fan, Yuyu,Tian, Dongping,Xu, Qinghao,et al. Particle swarm optimization based on K-means clustering and adaptive dual-groups strategy[J]. SWARM AND EVOLUTIONARY COMPUTATION,2026,100:45.
APA	Fan, Yuyu,Tian, Dongping,Xu, Qinghao,Sun, Jie,Xu, Qiu,&Shi, Zhongzhi.(2026).Particle swarm optimization based on K-means clustering and adaptive dual-groups strategy.SWARM AND EVOLUTIONARY COMPUTATION,100,45.
MLA	Fan, Yuyu,et al."Particle swarm optimization based on K-means clustering and adaptive dual-groups strategy".SWARM AND EVOLUTIONARY COMPUTATION 100(2026):45.