摘要
算法的收敛性分析,不仅对算法提供了理论支持,而且能够提高算法的实际应用效率.在随机搜索算法的基础上给出一些粒子群算法收敛性的证明方法,并且修正了一篇论文中两个不正确的收敛性证明.而且将粒子群算法的收敛性证明扩展到免疫粒子群算法中,证明了由张超等人提出的基于自适应搜索的免疫粒子群算法的收敛性,对于以后免疫粒子群算法的改进和使用有很大的帮助.
Convergence analysis of algorithm not only provides the theory support but also improves the application efficiency.On the basis of random search algorithm,the paper gives some ways of proving convergence of particle swarm optimization（PSO）,and corrects two mistakes that have appeared in a paper.The convergence proof of PSO extends into the immune particle swarm optimization algorithm,and proves the algorithm convergence of immune particle swarm optimization algorithm based on the adaptive search strategy provided by Zhang Chao.It has a great help to the improvement and use of the immune particle swarm optimization algorithm.
作者
平环
包兰英
姜静清
PING Huan;BAO Lanying;JIANG Jingqing(College of Mathematics,Inner Mongolia University for the Nationalities,Tongliao 028000,China;College of Computer Science and Technology,Inner Mongolia University for the Nationalities,Tongliao 028000,China)
出处
《湖北民族学院学报（自然科学版）》
CAS
2018年第1期29-32,共4页
Journal of Hubei Minzu University(Natural Science Edition)
基金
国家自然科学基金项目(61672301
61662057)
符号计算与知识工程教育部重点实验室(吉林大学)2016年度开放基金项目(93K172016K05)
关键词
自适应搜索
免疫粒子群
全局收敛性
adaptive search
immune particle swarm optimization algorithm
global convergence
