Abstract: Particle Swarm Optimization（PSO） is a novel stochastic global optimization evolutionary algorithm.To efficiently control the global search and local search of PSO and obtain a better balance between them.In this paper，a new particle swarm optimization is proposed based on the threshold of evolutionary generation，maximal focusing distance and Gaussian mutation among particles.The new algorithm includes Gaussian mutation operator during the running time，which，through the quantization decision of particles focusing degrees，can be very useful to improve the ability of PSO in breaking away from the local optimum.The experimental results show that the proposed algorithm is feasible and effective.

Key words: Particle Swarm Optimization（PSO）, global search, local search, maximal focusing distance, Gaussian mutation

摘要： 粒子群优化算法是一种新的随机全局优化进化算法。为了有效地控制其全局搜索和局部搜索，使之获得较好的平衡，论文在深入分析和研究标准粒子群优化算法的基础上，提出了一种基于进化代数阈值和粒子间最大聚集距离高斯变异的粒子群优化算法。该算法在运行过程中通过粒子聚集程度的量化判定，对当前的最优粒子施加高斯变异，从而增强粒子群优化算法跳出局部最优解的能力。测试函数仿真结果表明了该算法的可行性和有效性。

关键词: 粒子群优化算法, 全局搜索, 局部搜索, 最大聚集距离, 高斯变异