引用本文:刘朝华,张英杰,章兢,吴建辉.一种双态免疫微粒群算法[J].控制理论与应用,2011,28(1):65~72.[点击复制]
LIU Zhao-hua,ZHANG Ying-jie,ZHANG Jing,WU Jiang-hui.A novel binary-state immune particle swarm optimization algorithm[J].Control Theory and Technology,2011,28(1):65~72.[点击复制]
一种双态免疫微粒群算法
A novel binary-state immune particle swarm optimization algorithm
摘要点击 2263  全文点击 1551  投稿时间：2009-10-09  修订日期：2009-12-22
查看全文  查看/发表评论  下载PDF阅读器
DOI编号  10.7641/j.issn.1000-8152.2011.1.CCTA091271
  2011,28(1):65-72
中文关键词  微粒群  双态  精英学习  人工免疫系统  多模态函数
英文关键词  particle swarm optimization(PSO)  binary-state  elitist learning  artificial immune system(AIS)  multimodal function optimization
基金项目  国家自然科学基金重点资助项目(60634020); 湖南省科技计划重点资助项目(2010GK2022).
作者单位E-mail
刘朝华* 湖南大学 电气与信息工程学院
湖南大学 计算机与通信学院 		163liuzhaohua@163.com 
张英杰 湖南大学 计算机与通信学院  
章兢 湖南大学 电气与信息工程学院  
吴建辉 湖南大学 计算机与通信学院  
中文摘要
      针对基本微粒群算法的缺陷, 提出了一种双态免疫微粒群算法. 把微粒群分为捕食与探索两种状态, 处于捕食状态的精英粒子采用精英学习策略, 指导精英粒子逃离局部极值; 处于探索状态的微粒采用探索策略, 扩大解的搜索空间, 抑制早熟停滞现象. 同时引入免疫系统的克隆选择和受体编辑机制, 增强群体逃离局部极值及多模优化问题全局寻优能力. 实验表明该算法收敛速度快, 求解精度高, 尤其适合高维及多模态优化问题的求解.
英文摘要
      Conventional algorithms of particle swarm optimization(PSO) are often trapped in local optima in global optimization. A novel binary-state immune particle swarm optimization algorithm(BIPSO) is proposed. In order to enhance the explorative capacity of the algorithm while avoiding the premature stagnation behavior, the meta-heuristics allow for two behavior states of the particles including Gather State and Explore State during the search. The population is divided into two parts in iterations. Elitist learning strategy is applied to the elitist particle to help the jump out of local optimal regions when the search is identified to be in a gather state. This paper propose a concept of explore strategy to encourage particle in a explore state to escape from the local territory. They exhibit a wide range exploration. Moreover, in order to increase the diversity of the population and improve the search capabilities of PSO algorithm, the mechanism of clonal selection and the mechanism of receptor edition are introduced into this algorithm. Experiments on several benchmarks show that the proposed method is capable of improving the search performance. It is efficient in tackling the high dimensional multimodal optimization problems.