一种改进的粒子群优化算法

An Improved Particle Swarm Optimization Algorithm

为解决标准粒子群优化算法收敛速度慢、容易陷入局部最优的问题,提出了一种基于标准粒子群优化算法的改进算法.通过对标准粒子群算法的速度和位置更新公式的修改,增强了粒子在搜索后期的多样性,提高了全局搜索能力,降低了陷入局部最优的可能性.用3个基准函数对改进算法进行验证,比较分析表明:NPSO与SPSO比,收敛速度明显提高,与可能出现不收敛的CFM比,一致收敛.

In order to solve the problem of slow convergence and easy to fall into local optimum,an improved algorithm based on the standard particle swarm optimization algorithm is proposed.By modifying the velocity update formula and the position update formula of the standard particle swarm optimization algorithm,the diversity of the particle in the late period of search is improved,and the global searching ability is improved,and the possibility of falling into local optimum is reduced.3benchmark functions are used to validate the improved algorithm,and the results are compared with the other two particle swarm optimization algorithms.The experimental results show that the new algorithm has some improvement in the convergence rate and the local optimum.