关键词: 粒子群优化, 免疫克隆, 个性化变异, 种群多样性

Abstract: In order to overcome prematurity and low searching speed of Particle Swarm Optimization（PSO） algorithm，a novel immune clone PSO algorithm with personalized mutations is proposed according to the immune clone selection theory.In this modified algorithm，clone selection operator is applied to raise the diversity of PSO，vaccine heuristic mutation，cauchy mutation and symmetrical mutation are applied to low fitness subpopulation to speed up the convergence and enhance the capabilities of escaping local optimum，normal distribution mutation and improved chaos disturbance are applied to high fitness subpopulation to improve the accuracy of algorithm.Meanwhile，the clone number of antibody，the mutation and crossover ratios can regulate automatically in the improved algorithm.The experiment results demonstrate that the proposed algorithm is superior to several typical modified PSO algorithms and immune clone algorithm.

Key words: particle swarm optimization, immune clone, personalized mutation, population diversity