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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.
Aiming at the disadvantage that Particle Swarm Optimization (PSO) is easy to fall into the local optimum and the convergence speed is slow, two types of mutation operations based on the normal distribution are added in the process of PSO updating. One kind of mutation is used to enhance the local search ability , And the other kind of mutation is used to improve the ability to find the global optimal point and avoid all the particles falling into the neighborhood of a local optimal point.Numerical results show that the global search ability of the proposed algorithm is significantly improved and the convergence speed is faster.