A Glowworm Swarm Optimization Algorithm with Improved Movement Rule

Author

He, Lifang ; Tong, Xiong ; Huang, Songwei

Author_Institution

Dept. of Electron. Inf., Kunming Univ. of Sci. & Technol., Kunming, China

fYear

2012

fDate

1-3 Nov. 2012

Firstpage

109

Lastpage

112

Abstract

At present, Glowworm Swarm Optimization (GSO) algorithm is a popular swarm intelligent optimization algorithm that has been used in many fields. However, basic GSO algorithm is easy to fall into local optimum, and has low accuracy and low speed of convergence in the later period. Thus, a new GSO algorithm is presented in this paper, in which Tent map of chaos is applied for the deployment of glowworms and a new movement rule is proposed. The simulation results prove that the effectiveness of the new GSO algorithm in the capture of the global optimum of several test functions, and the speed of convergence and accuracy are improved, compared with basic GSO algorithm.

Keywords

optimisation; swarm intelligence; GSO algorithm; glowworm swarm optimization algorithm; improved movement rule; swarm intelligent optimization algorithm; test functions; Accuracy; Algorithm design and analysis; Chaos; Convergence; Linear programming; Optimization; Particle swarm optimization; Deployment of glowworms; Glowworm Swarm Optimization (GSO); Movement rule; Tent map;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Networks and Intelligent Systems (ICINIS), 2012 Fifth International Conference on

Conference_Location

Tianjin

Print_ISBN

978-1-4673-3083-1

Type

conf

DOI

10.1109/ICINIS.2012.16

Filename

6376497