控制理论与应用2011，Vol.28Issue(1)：65-72,8.
一种双态免疫微粒群算法
A novel binary-state immune particle swarm optimization algorithm
摘要
Abstract
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.关键词
微粒群/双态/精英学习/人工免疫系统/多模态函数Key words
particle swarm optimization(PSO)/ binary-state/ elitist learning/ artificial immune system(AIS)/ multimodal function optimization分类
信息技术与安全科学引用本文复制引用
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):65-72,8.基金项目
国家自然科学基金重点资助项目(60634020) （60634020）
湖南省科技计划重点资助项目(2010GK2022). （2010GK2022）
