关键词: 粒子群优化算法, 混沌, 惯性权重, 变异

Abstract: A new Particle Swarm Optimization（PSO） algorithm is proposed based on three aspects of improvement in standard PSO to solve the problems about premature convergence and low precision.The iteration formula of PSO based on the simple PSO which removes the velocity parameter is applied.Inertia weight，an important factor in PSO，is determined using a controlled chaotic variable to enhance the balance of global and local search of algorithm.The mutation operators are introduced to adjust individual and global optimal to improve the search performance of algorithm.The simulation experiments show that the proposed algorithm not only has great advantages of convergence property over standard PSO and some other modified PSO algorithms，but also effectively avoids being trapped in local minima.

Key words: Particle Swarm Optimization（PSO）, chaos, inertia weight, mutation