Abstract: A new particle swarm optimization (PSO) algorithm was presented to overcome disadvantages that standard PSO has shown in solving complex functions,including slow convergence rates,low precisions and premature convergence,etc．The proposed algorithm improves the performances of standard PSO by following methods:a) applying chaotic initialization for swarm,b) using adaptive inertia weight to enhance the balance of global and local search of algorithm,and c) introducing disturbance factors to avoid being trapped in local optimum．The experimental results show that the new algorithm has great advantages of convergence property than standard PSO and some other modified algorithms．

Key words: PSO,Chaotic initialization,Inertia weight,Disturbance factors