Integrated Learning Particle Swarm Optimization Algorithm Based on Clustering

Kexin Lin, Tie Cai, Hui Wang, Wei Li and Cao Wei
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Kexin Lin: Shenzhen Institute of Information Technology, China
Tie Cai: Shenzhen Institute of Information Technology, China
Hui Wang: Shenzhen Institute of Information Technology, China
Wei Li: Jiangxi University of Science and Technology, China
Cao Wei: Shenzhen Institute of Information Technology, China

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2025, vol. 19, issue 1, 1-24

Abstract: The particle swarm optimization algorithm is widely recognized for its few adjustable parameters and high flexibility. However, it still has some limitations. This paper proposes an integrated learning particle swarm optimization algorithm based on clustering, referred to as ILPSO-C. Using the adaptive learning rate clustering strategy, the population is dynamically divided into multiple subpopulations, each of which adjusts its learning rate based on the problem characteristics. The algorithm's adaptability is further improved through an adaptive parameter tuning strategy. A multimodal learning interaction mechanism introduces information sharing between subpopulations. Finally, a stalled activation strategy encourages the algorithm to escape local optima. The performance of ILPSO-C is evaluated on the CEC 2017 and CEC 2022 benchmark suites and the economic dispatch problem of power systems. Experimental results show that ILPSO-C outperforms other algorithms.

Date: 2025
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