关键词: 粒子群算法, 人工蜂群, 蛙跳算法

Abstract: Since the standard Particle Swarm Optimization（PSO） algorithm is easy to fall into local optimum and converges slowly, a novel particle swarm optimization algorithm combined with the search operator of artificial bee colony algorithm and that of shuffled frog leaping algorithm is proposed. Firstly, use the search operator of artificial bee colony algorithm to improve the ability of explore to avoid falling into local optimum；Secondly, introduce the update operator of shuffled frog leaping algorithm into PSO to enhance the accuracy of convergence. Through the twelve standard test functions simulation experiments and compared with other algorithms, experiment results show that the proposed algorithm can avoid falling into local optimization and significantly improve convergence speed.

Key words: Particle Swarm Optimization（PSO）, artificial bee colony optimization, shuffled frog leaping algorithm