The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems

Author

Nootyaskool, Supakit

Author_Institution

Fac. of Inf. Technol., King Mongkut´´s Inst. of Technol. Ladkrabang, Bangkok, Thailand

fYear

2012

fDate

7-8 July 2012

Firstpage

57

Lastpage

61

Abstract

Genetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly.

Keywords

genetic algorithms; particle swarm optimisation; search problems; GA; PSO; exploration search; five-difference complexity; genetic algorithm; hybrid implementation genetic algorithm; multimodal problem; numerical functions; particle swarm optimization; unconstrained optimization problems; unimodal problem; IP networks; Noise; Numerical models; genetic algorithm; hybrid technique; numerical optimization; particle swarm optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge and Smart Technology (KST), 2012 4th International Conference on

Conference_Location

Chonburi

Print_ISBN

978-1-4673-2166-2

Type

conf

DOI

10.1109/KST.2012.6287739

Filename

6287739