A dynamic inertia weight particle swarm optimization algorithm

被引：194

作者：

Jiao, Bin ^{[1
,2
]}

Lian, Zhigang ^{[1
]}

Gu, Xingsheng ^{[1
]}

机构：

[1] E China Univ Sci & Technol, Res Inst Automat, Shanghai 200237, Peoples R China

[2] Shanghai DianJi Univ, Dept Elect Engn, Shanghai 200240, Peoples R China

来源：

CHAOS SOLITONS & FRACTALS | 2008年 / 37卷 / 03期

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1016/j.chaos.2006.09.063

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly. (c) 2006 Elsevier Ltd. All rights reserved.

引用

页码：698 / 705

页数：8

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