A Hybrid Variants Particle Swarm Optimization Algorithm

Author

Zhao, Xiaojuan

Author_Institution

Dept. of Inf. Eng., Hunan Urban Constr. Coll., Xiangtan, China

fYear

2010

fDate

24-25 April 2010

Firstpage

33

Lastpage

36

Abstract

A hybrid variants particle swarm optimization (HVPSO) algorithm is proposed in this paper. HVPSO is a stand particle swarm optimization algorithm including the popular genetic algorithm operator, cross-over and root mean square (RMS) variants to make the convergence faster. Two different HVPSO algorithms are considered in this paper: the first one is the conventional PSO (cPSO) and the second is the global-local best values based PSO (GLbest-PSO). In order to compare and verify the validity and effectiveness of the new approaches for PSO, several statistical analyses are carried out. The results clearly demonstrate that the GLbest-PSO provides better results for all the cross-over variants than that of the cPSO.

Keywords

Acceleration; Birds; Educational institutions; Genetic algorithms; Machine vision; Man machine systems; Marine animals; Optimal control; Particle swarm optimization; Root mean square; global-local best; hybrid variants; particle swarm optimization; root mean square;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Vision and Human-Machine Interface (MVHI), 2010 International Conference on

Conference_Location

Kaifeng, China

Print_ISBN

978-1-4244-6595-8

Electronic_ISBN

978-1-4244-6596-5

Type

conf

DOI

10.1109/MVHI.2010.80

Filename

5532622