带两类正态变异的PSO 算法

针对基本粒子群优化算法(PSO)容易陷入局部最优点和收敛速度较慢的缺点，提出在PSO更新过程中加入两类基于正态分布投点的变异操作。一类变异用来增强局部搜索能力，另一类变异用来提高发现全局最优点的能力，避免所有粒子陷入到一个局部最优点的邻域内。数值结果表明，所提出算法的全局搜索能力有显著提高，并且收敛速度更快。

Improved PSO with two mutations based on normally throwing points distribution

The basic particle swarm optimization algorithm(PSO) is easy to fall into local minima and convergence slowly, so an improved PSO algorithm with two mutations based on normally throwing distribution points in the updating process is presented. One of the mutations is used to enhance the local searching ability, the other is used to increase the ability of finding the global optimum and avoid all particles falling into a neighborhood of a local minima. Experimental results show that the global search capability of the proposed algorithm is improved significantly and the convergence speed is faster.