An Improved Particle Swarm Optimization Method Based on Chaos

This paper proposes a new particle swarm optimization method that use chaotic maps for parameter adaptation. To enhance the performance of particle swarm optimization, which is an evolutionary computation technique through individual improvement plus population cooperation and competition, a modified particle swarm optimization algorithm is proposed by incorporating chaos(CPSO). Firstly, diversity measure method is introduced into PSO to efficiently balance the exploration and exploitation abilities. Secondly, chaotic searching strategy is introduced when the population is trapped into local optimum. Experiment results and comparisons with the standard PSO and GA show that the CPSO can effectively enhance the searching efficiency and greatly improve the searching quality.