%0 Journal Article
%T Optimization algorithm with stochastic focusing search<br>随机聚焦搜索优化算法
%A ZHENG Yongkang
%A CHEN Weirong
%A DAI Chaohua
%A WANG Weibo
%A <br>郑永康
%A 陈维荣
%A 戴朝华
%A 王维博
%J 控制理论与应用
%D 2009
%I 
%X A novel optimization algorithm with stochastic focusing search(SFS) is proposed. This algorithm is a swarmintelligence algorithm, which imitates the random action in human searching behaviors. The algorithm performance is studied by using a set of typical complex functions, and is compared with that of the differential evolution(DE) algorithm and the comprehensive learning-particle-swarm-optimizer(CLPSO) algorithm. The simulation results show that SFS solves most of the benchmark problems and can be considered a promising candidate of search algorithms when the existing algorithms have difficulties in solving some problems.
%K swarm intelligence
%K stochastic focusing search
%K human randomized searching
%K particle swarm optimization<br>群集智能
%K 随机聚焦搜索
%K 人类随机搜索
%K 粒子群优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=FC5BFDD3E088F9428A679752A6B31D81&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=5D311CA918CA9A03&sid=BD77137A0285B6FF&eid=3D9E2C3DB640307A&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=5