关键词: 随机微粒群算法 单纯形法 全局优化

Abstract:

Based on the stochastic particle swarm optimization algorithm that guarantees global convergence, an improved stochastic particle swarm optimization a  lgorithm named SM-SPSO is proposed. During the evolution of SPSO, the best particle produced by the simplex method substitutes for the stopping particle  , and takes part in the evolution of the next generation. Thus, both the characteristics of speedy convergence of the nonlinear simplex method and the g  lobal convergence of the stochastic particle swarm optimization algorithm are used. Through the experiments of two multi-modal test functions, the resul t of simulation proves that the speed of convergence and the rate of convergence for SM-SPSO are better than SPSO on the same dimension of search space.

Key words: stochastic particle swarm optimization;simplex method;global optimization