关键词: 粒子群优化算法, 交叉, 变异, 混合

Abstract: Particle swarm optimization（PSO） is a global optimization algorithm based on swarm intelligence theory，and search the problem space effectively through cooperation and competition among the individuals of the population.Aiming at the shortcoming of basic PSO algorithm，that is slow convergence rate at ending and easily plunging into the local optimum，a new hybrid PSO is proposed.By changing the method of initialization and adding the crossover and mutation to the algorithm，the hybrid PSO’s performance is significant improved.Experimental results indicate that the modified PSO has good behavior both on improving the global convergence ability and enhancing convergence rate.

Key words: particle swarm optimization, crossover, mutation, hybrid