An Adaptive Hybrid Particle Swarm Optimization

Author

Liu, Yong ; Liang, Fangfang

Author_Institution

Inst. of Intell. Vision & Image Inf., China Three Gorges Univ., Yichang, China

Volume

1

fYear

2009

fDate

12-14 Dec. 2009

Firstpage

87

Lastpage

90

Abstract

The particle swarm optimization (PSO) algorithm is vulnerable to reach local optimal value. So, this paper presents an adaptive hybrid particles swarm optimization. During the solving process, both crossover operator in genetic algorithm and hyper-mutation are introduced. Referring to the selection mechanism of immune algorithm based on information entropy, the adaptive selections mechanism is proposed. Experiments show that the algorithm effectively improves global search capability.

Keywords

entropy; genetic algorithms; particle swarm optimisation; adaptive hybrid PSO algorithm; adaptive selection mechanism; crossover operator; genetic algorithm; hyper-mutation; immune algorithm; information entropy; particle swarm optimization; Algorithm design and analysis; Biological system modeling; Biology computing; Competitive intelligence; Computational intelligence; Convergence; Genetic algorithms; Immune system; Information entropy; Particle swarm optimization; Cross; Hyper-mutation; Particle Swarm Optimization Algorithm; immune algorithm based on information entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence and Design, 2009. ISCID '09. Second International Symposium on

Conference_Location

Changsha

Print_ISBN

978-0-7695-3865-5

Type

conf

DOI

10.1109/ISCID.2009.29

Filename

5370451