Particle Swarm Optimization with Average Individuals Distance-Incorporated Exploitation

Qingya Sui, Lin Zhong, Jiatianyi Yu, Haotian Li, Zhenyu Lei, Shangce Gao*

*この論文の責任著者

研究成果: ジャーナルへの寄稿Letter査読

1 被引用数 (Scopus)

抄録

Particle swarm optimization (PSO) is a popular optimization technique known for its simplicity and effectiveness. This paper introduces a variant that achieves a better balance between exploration and exploitation, named DiPSO. DiPSO incorporates a novel strategy based on trends in mean distance between individuals for local exploitation control. Experiments on 29 benchmark functions demonstrate that DiPSO consistently outperforms the state-of-the-art variant of PSO. Convergence analysis reveals that DiPSO achieves faster convergence and superior solutions. These results highlight the effectiveness of DiPSO in solving optimization problems.

本文言語英語
ページ(範囲)1722-1724
ページ数3
ジャーナルIEEJ Transactions on Electrical and Electronic Engineering
18
10
DOI
出版ステータス出版済み - 2023/10

ASJC Scopus 主題領域

  • 電子工学および電気工学

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