%0 Journal Article
%T Extended particle-swarm optimization algorithm<br>扩展的微粒群算法
%A MO Si-min
%A ZENG Jian-chao
%A XIE Li-ping
%A <br>莫思敏
%A 曾建潮
%A 谢丽萍
%J 控制理论与应用
%D 2012
%I 
%X The interaction among particles is a key factor affecting the performance of particle swarm optimization (PSO) algorithm. To overcome the premature convergence, an extended particle swarm optimization (EPSO) algorithm is proposed, in which the interaction mechanism among particles is redefined based on the idea of attraction and repulsion forces in Artificial Physics. Furthermore, the rule of attraction and repulsion among particles is defined by comparing particle fitness values. To look for the global optimum, each particle randomly moves along the direction of the resultant force produced by all particles. Simulation results show that EPSO algorithm effectively improves the global performances of other related algorithms.
%K particle-swarm optimization algorithm
%K artificial physics
%K attraction and repulsion force rule<br>微粒群算法
%K 拟态物理学
%K 引斥力规则
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=A56F1C7481D474086F35C1F551F88A53&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=B31275AF3241DB2D&sid=A9C78B2A6AAEEAAD&eid=43AADF4B53A8BF6F&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=0