计算机应用研究Issue(11)：3229-3233,5.DOI:10.3969/j.issn.1001-3695.2014.11.007
免疫综合学习粒子群优化算法
Immune comprehensive learning particle swarm optimization algorithm
摘要
Abstract
Convergence of the comprehensive learning particle swarm optimization(CLPSO)algorithm is relatively slow at the late stage of evolution.Once all particles trapped in local optimum,the algorithm can not jump out of the local optimum.This paper proposed immune comprehensive learning particle swarm optimization(ICLPSO)algorithms.The algorithm introduced clonal se-lection mechanism in artificial immune system.Using of clonal copy,hypermutation and clonal selection,it increased the diversi-ty of the population,improved the convergence rate and enhanced the ability of escape from the local optimum and multi-mode op-timization ability of global optimization.Using the elitist learning strategy,the ability to escape from local optimia is further en-hanced.Experiments on several benchmark functions verify the effective of the proposed algorithm.关键词
综合学习粒子群算法(CLPSO)/人工免疫系统/精英学习/函数优化Key words
comprehensive learning particle swarm optimization algorithm/artificial immune system/elitist learning/function optimization分类
信息技术与安全科学引用本文复制引用
林国汉,章兢,刘朝华..免疫综合学习粒子群优化算法[J].计算机应用研究,2014,(11):3229-3233,5.基金项目
国家自然科学基金资助项目 （）
国家教育部博士点基金资助项目 （）
湖南省自然科学基金资助项目 （）
