Study Of Particle Swarm Optimization Based On Cellular Automata

Particle swarm optimization(PSO) is a class of swarm-based stochastic optimization algorithm originating from artificial life and evolutionary computation.The algorithm performs the optimization through following the personal best solution of each particle and the global best one of the whole swarm.PSO can be programmed easily and few parameters need to be tuned.It has been successfully applied in many areas.In this paper , Associating the modeling idea of cellular automata(CA), PSO can be described as a CA,and then by using the neighborhood and some theories of CA,we proposed a niche PSO algorithm based on CA;Then we analysed the necessity of introducing neighborhood of CA,and carried through systemic analysis and research in the light of basis neighborhood of the Lbest Model on PSO and neighborhood of CA,Although, previous investigation within PSO algorithm has found that the effect of neighborhood topologies interacted with the function being optimized,the research presented in the paper has identified some superior neighborhood topologies that result in best performance on a range of functions;At last,by analyzing the relation among of the Lbest Model on PSO,an improved PSO based on neighborhood and standard PSO , we presented an improved PSO based on the extended Moore neighborhood of CA which has been improved to be a superior neighborhood topology on a range of functions being optimized.