Particle swarm optimization based on K-means clustering and adaptive dual-groups strategy
文献类型：期刊论文
| 作者 | Fan, Yuyu2; Tian, Dongping2; Xu, Qinghao2; Sun, Jie3; Xu, Qiu1; Shi, Zhongzhi4 |
| 刊名 | SWARM AND EVOLUTIONARY COMPUTATION
 |
| 出版日期 | 2026 |
| 卷号 | 100页码:45 |
| 关键词 | Particle swarm optimization Twin swarm collaboration Comprehensive learning,Probability-driven elite replacement,Competitive disturbance mechanism |
| ISSN号 | 2210-6502 |
| DOI | 10.1016/j.swevo.2025.102226 |
| 英文摘要 | 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. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001626836100002 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/43085]  |
| 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.King Abdullah Univ Sci & Technol, Phys Sci & Engn Div, Thuwal 23955, Saudi Arabia 2.Baoji Univ Arts & Sci, Inst Comp Software, Baoji 721007, Shaanxi, Peoples R China 3.Changji Univ, Sch Informat Engn, Changji 831100, Xinjiang, Peoples R China 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. |
入库方式： OAI收割
来源：计算技术研究所
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