Title :
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
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;
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
DOI :
10.1109/ICIAS.2007.4658347
