中文摘要：

针对传统粒子群算法初期收敛较快，而在后期容易陷入早熟、局部最优的特点，提出了一种新的混沌粒子群优化算法，不同于己有的混沌粒子群算法的简单粒子序列替换，该算法将混沌融入到粒子运动过程中，使粒子群在混沌与稳定之间交替运动，逐步向最优点靠近。并提出了一种新的混沌粒子群数学模型，进行了非线性动力学分析。数值测试结果表明该方法能跳出局部最优，极大提高了计算精度，进一步提高了全局寻优能力。

英文摘要：

The original particle swarm optimization （PSO） algorithm has the advantages of fast convergence, but with the shortcoming of premature and local convergence. To overcome this problem, a new chaos-particle swarm optimization algorithm was presented, which was different from the conventional method of replacing pre-particle. Instead, the algo- rithm in this paper made the motion of particles with characteristics of chaos, so as to make particles move between the state of chaos and stable, and gradually close to the optimal value. The nonlinear dynamics of the proposed Mathematical model are analyzed, and the results of the experiment show that the proposed algorithm can result in encouraging results.