Particle swarm optimization algorithm tends to fall into local optimum sometimes.To resolve this problem,an improved particle swarm optimization algorithm based on two kinds of different chaotic maps is proposed.The algorithm produces primitive chaotic particle swarm using the uniform distribution of Tent map and improves the diversity of search.When the particle swarm evolves to a local optimum,the chaotic mutation operator produced by Logistic map is adopted to form a disturbance on the swarm to drive particle swarm jump out of local optimum and approach the global optimum.Meanwhile,an adaptive inertia weight factor is introduced to adjust particles inertia weight factor adaptively,which forms a new 2-chaotic maps embedded adaptive particle swarm optimization algorithm(2-CMEAPSO)that can fully utilize the randomness and ergodicity of the chaotic motion to enhance optimization capability.Experimental results show that the improved algorithm can efficiently overcome the premature of standard particle swarm optimization algorithm.Besides,it has stronger global optimization ability and higher accuracy than the basic particle swarm optimization algorithm.