A LH-DM Strategy Based Particle Swarm Optimization Algorithm

被引：0

作者：

Liu, W. ^{[1
]}

Zhou, J. ^{[1
]}

机构：

[1] Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON COMPUTER INFORMATION SYSTEMS AND INDUSTRIAL APPLICATIONS (CISIA 2015) | 2015年 / 18卷

关键词：

premature convergence; dynamic mutation; particle diversity;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Aiming at the premature problem of the Particle Swarm Optimization (PSO), an improved algorithm based on dynamic mutation strategy named Lowdiversity and Highdiversity Dual Mutation Factors Particle Swarm Optimization (LH-DMPSO) was proposed. The dynamic mutation strategy enhanced particle diversity, avoid falling into local optimal position, simulation results based on the Benchmark functions shows that the improved algorithm exceeds the standard PSO and Gaussian Mutation Particle Swarm Optimization (GMPSO) on convergence and stability.

引用

页码：55 / 58

页数：4

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