关键词: 微粒群算法, 混合蛙跳算法, 信息替换策略, 随机扰动, 协作方式

Abstract: Concerning the premature convergence of Particle Swarm Optimization (PSO) algorithm and Shuffled Frog Leaping Algorithm (SFLA), this paper proposed a swarm intelligence optimization algorithm based on the combination of SFLA and PSO. In this algorithm, the whole particle was divided into two equal groups: SFLA and PSO. An information replacement strategy was designed in the process of their iteration: comparing the fitness of PSO with that of SFLA, the worst individual in each subgroup of SFLA would replace some better individuals in PSO when SFLA is better; otherwise, some better individuals in PSO would replace the best individual in each subgroup of SFLA. Meanwhile, a collaborative approach between the two groups was also designed. Since the information replacement strategy could be influenced by the premature convergence problem in PSO, a random disturbance would be given on each particle's best position. The simulation results show that the proposed algorithm can improve the global search ability and convergence speed efficiently. For the complex functions with high-dimension, the algorithm has very good stability.

Key words: Particle Swarm Optimization (PSO), Shuffled Frog Leaping Algorithm (SFLA), information replacement strategy, random disturbance, collaborative approach