Improved particle swarm optimization with dynamic fractional order velocity and wavelet mutation

被引：2

作者：

Zhou, Lingyun ^{[1
,2
]}

Ding, Lixin ^{[1
]}

机构：

[1] State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China

[2] College of Computer Science, South-Central University for Nationalities, Wuhan,Hubei,430074, China

来源：

International Journal of Hybrid Information Technology | 2016年 / 9卷 / 05期

基金：

中国国家自然科学基金;

关键词：

D O I：

10.14257/ijhit.2016.9.5.11

中图分类号：

学科分类号：

摘要：

Particle Swarm Optimization (PSO) is one of the most powerful algorithms for optimization. Traditional PSO algorithm tends to suffer from slow convergence and trapping into local optimum. In this paper, an improved PSO algorithm is proposed by combining dynamic fractional order technology and the wavelet mutation strategy. In the proposed method, a dynamic fractional order velocity update equation is designed to control the convergence rate. Furthermore, the wavelet mutation mechanism is employed to improve the swarm diversity and escape from the local optimums. The experimental results show that the proposed algorithm can provide fast convergence speed and high convergence precision based on the ten classic test functions. © 2016 SERSC.

引用

页码：131 / 144