%0 Journal Article
%T Group Search Optimizer Applying Opposition-based Learning<br>应用反向学习策略的群搜索优化算法
%A WANG Shen-wen
%A DING Li-xin
%A XIE Da-tong
%A SHU Wan-neng
%A XIE Cheng-wang
%A YANG Hua
%A <br>汪慎文
%A 丁立新
%A 谢大同
%A 舒万能
%A 谢承旺
%A 杨华
%J 计算机科学
%D 2012
%I 
%X Group search optimizer(GSO)is a new swarm intelligence algorithms based on the producer-scrounger model.GSO has been shown to yield good performance for solving various optimization problems. However, it tends to suffer from premature convergence and get stuck in local minima. This paper proposed an enhanced GSO algorithm called GOGSO, which employs generalized opposition-based learning to transform the current population into a new opposition population and uses an elite selection mechanism on the two populations. xperiments were conducted on a comprehensive set of benchmark functions. The results show that OGSO obtains promising performance.
%K Group search optimizer
%K Opposition-based learning
%K Numerical optimization<br>群搜索优化算法
%K 反向学习
%K 数值优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=75AFC294F2AFDCB1D0A2BED77E00983C&yid=99E9153A83D4CB11&vid=7C3A4C1EE6A45749&iid=9CF7A0430CBB2DFD&sid=DD74772618543076&eid=3E0812ED84A7B31D&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0