Particle Swarm Optimization with Dynamic Inertia Weight and Mutation

被引：11

作者：

Liu, Xuedan ^{[1
]}

Wang, Qiang ^{[1
]}

Liu, Haiyan ^{[1
]}

Li, Lili ^{[2
]}

机构：

[1] Guangxi Normal Univ, Coll Comp Sci & Informat Engn, Guilin, Peoples R China

[2] Guangxi Hezhou Coll, Dept Phys & Elect Informat Engn, Hezhou, Peoples R China

来源：

THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING | 2009年

关键词：

particle swarm optimization; inertia weight; convergence rate; constrained layout optimization;

D O I：

10.1109/WGEC.2009.99

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Particle Swarm Optimization (PSO) plunges into the local minimum easily. In order to overcome this shortcoming, we propose an improved PSO algorithm with the features of linearly decreasing of inertia weight and the re-initialization of the particle when it gets stagnated. The improved PSO is a local PSO and its topology is wheels. From the experimental results of three non-linear testing functions and a problem with non-convex solution space, it is obvious that the improved PSO algorithm greatly enhances the rate of global convergence.

引用

页码：620 / +

页数：2

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