%0 Journal Article
%T Ant-colony-genetic algorithm with adaptive parameters based on grey prediction and normal cloud<br>基于灰预测和正态云的参数自适应蚁群遗传算法
%A MU Feng
%A YUAN Xiao-hui
%A WANG Ci-guang
%A JING Yun
%A <br>牟峰
%A 袁晓辉
%A 王慈光
%A 景云
%J 控制理论与应用
%D 2010
%I 
%X Ant colony algorithm with positive feedback has a good capability of global convergence; while the genetic algorithm(GA) is with a fast performance in global search. A hybrid algorithm with adaptive parameters is proposed to take advantages of the above two optimization algorithm. Using the grey prediction, we obtain in the ant colony strategy the estimates of the maximum (minimum) trail limits which are controlled for avoiding the immature convergence. Meanwhile, we employ the cloud models to build a set of association rules which are used to adaptively adjust algorithm parameters by information feedback during the iterative process, thus reducing the reliance on initial parameters. Simulation results for job-shop scheduling problem(JSP) and traveling salesman problem(TSP) validate the algorithm.
%K hybrid algorithm
%K max-min ant system(MMAS)
%K genetic algorithm (GA)
%K normal cloud
%K grey prediction<br>混合算法
%K 最大最小蚂蚁系统
%K 遗传算法
%K 正态云
%K 灰预测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=B6DDDC89A500CF1607A6B7B14EEC8526&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=B31275AF3241DB2D&sid=80BBC722D530DB8D&eid=E008F9AD6D4B96EF&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=16