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

摘要： 为了克服粒子群算法易早熟、后期收敛慢的缺点，根据免疫优化理论，提出一种改进的个性化变异免疫粒子群算法。该算法通过对适应度较低的弱势抗体群采用疫苗启发式变异、柯西变异和对称变异，加快了算法收敛速度，增强了算法逃离局部最优的能力;通过对适应度较高的记忆抗体群采用正态变异和改进的混沌扰动，提高了算法的收敛精度。同时，算法中的交叉变异率均实行自适应调整。实验结果表明该算法优于几种典型的粒子群算法和基本免疫克隆算法。

关键词: 粒子群优化, 免疫克隆, 个性化变异, 种群多样性