A new method to improve the particle swarm optimization using cellular learning automata (CLAPSO)

Particle swarm optimization (PSO) is a population based statistical optimization technique which inspired by social behavior of bird flocking or fish schooling. PSO algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. The main weakness of PSO especially in multi modal problems is trapping in local optimums. This paper presents an improved particle swarm optimization algorithm (CLAPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight. Experimental results indicate that the CLAPSO improves the search performance on the benchmark functions significantly.