Particle swarm optimization with Gaussian mutation

被引：386

作者：

Higashi, N ^{[1
]}

Iba, H ^{[1
]}

机构：

[1] Univ Tokyo, Dept Frontier Informat, Grad Sch Frontier Sci, Bunkyo Ku, Tokyo 1138656, Japan

来源：

PROCEEDINGS OF THE 2003 IEEE SWARM INTELLIGENCE SYMPOSIUM (SIS 03) | 2003年

关键词：

D O I：

10.1109/SIS.2003.1202250

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we present Particle Swarm Optimization with Gaussian Mutation combining the idea of the particle swarm with concepts from Evolutionary Algorithms. This method combines the traditional velocity and position update rules with the ideas of Gaussian Mutation. This model is tested and compared with the standard PSO and standard GA. The comparative experiments have been conducted on unimodal functions and multimodal functions. PSO with Gaussian Mutation is able to obtain the result superior to GA. We also apply the PSO with Gaussian Mutation to a gene network. Consequently, it has succeeded in acquiring the better results than those by GA and PSO alone.

引用

页码：72 / 79

页数：8