Particle Swarm Optimization with Average Individuals Distance-Incorporated Exploitation

被引：1

作者：

Sui, Qingya ^{[1
]}

Zhong, Lin ^{[1
]}

Yu, Jiatianyi ^{[1
]}

Li, Haotian ^{[1
]}

Lei, Zhenyu ^{[1
]}

Gao, Shangce ^{[1
]}

机构：

[1] Univ Toyama, Fac Engn, Toyama 9308555, Japan

来源：

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING | 2023年 / 18卷 / 10期

关键词：

particle swarm optimization; exploration and exploitation; evolutionary information;

D O I：

10.1002/tee.23896

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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. (c) 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

引用

页码：1722 / 1724

页数：3

共 3 条

[1] Particle Swarm Optimization: A Comprehensive Survey [J].