A hybrid method for optimization (discrete PSO + CLA)

Author

Jafarpour, B. ; Meybodi, M.R. ; Shiry, S.

Author_Institution

Comput. Eng. & Inf. Technol. Dept., Amirkabir Univ. of Technol., Tehran

fYear

2007

fDate

25-28 Nov. 2007

Firstpage

55

Lastpage

60

Abstract

PSO is an evolutionary algorithm that is inspired from collective behavior of animals such as fish schooling or bird flocking. One of the drawbacks of this model is premature convergence and trapping in local optima. In this paper we propose a solution to this problem in discrete version of PSO that uses Learning Automata and introduce a cellular learning automata (CLA) based discrete PSO. Experimental results on five optimization problems show the superiority of the proposed algorithm.

Keywords

cellular automata; evolutionary computation; learning automata; particle swarm optimisation; cellular learning automata; discrete PSO; evolutionary algorithm; particle swarm optimization; Birds; Educational institutions; Evolutionary computation; Hybrid intelligent systems; Information technology; Learning automata; Marine animals; Optimization methods; Particle swarm optimization; Testing; Cellular Learning Automata; Learning Automata; Optimization; Particle Swarm Optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent and Advanced Systems, 2007. ICIAS 2007. International Conference on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4244-1355-3

Electronic_ISBN

978-1-4244-1356-0

Type

conf

DOI

10.1109/ICIAS.2007.4658347

Filename

4658347