中文摘要：

粒子群算法收敛速度快,规则简单,但易陷入局部极值。在粒子群算法中引入混沌序列,提出一种优化策略,以分阶段的思想进行寻优,使其在搜索初期更具遍历性,在搜索中后期,通过人为改变个别粒子的速度和位置,使算法具有更快的收敛速度与更好的全局搜索能力。在此基础上,提出一种改进Tent映射的策略,并将优化策略分别应用于基于Logistic映射的粒子群和改进的Tent映射的粒子群,同标准粒子群算法在寻优速度、精度、成功率等方面进行仿真与比较。

英文摘要：

Particle Swarm Optimization（PSO） is an evolution algorithm by simulating birds feeding. Its convergence speed is fast,and the regulation is simple. But it’s easy to be trapped into local extremum. The improved Chaos Particle Swarm Optimization Algorithm was proposed. At the initial searching stage,the algorithm has better ergodicity. At the medium stage and later stage,the algorithm has faster convergence speed and better global searching capability through changing a certain particle’s speed and position. The LogisticPSO,the improved TentPSO and the standard PSO were compared with each other in the speed,accuracy and successful ratio of optimization.