%0 Journal Article
%T Binary ant colony algorithm with controllable search bias<br>可控搜索偏向的二元蚁群算法
%A HU Gang
%A XIONG Wei-qing
%A ZHANG Xiang
%A YUAN Jun-liang
%A <br>胡钢
%A 熊伟清
%A 张翔
%A 袁军良
%J 控制理论与应用
%D 2011
%I 
%X Ant colony algorithm explores the solution space according to the bias produced by pheromone trail. However, most of the existing improvements concentrate in raising the population diversity, instead of controlling the search bias. On the basis of the controllable search bias and by the update pattern of the current pheromone, we determine for any given iteration the lower bound of the probability of no further improvement in solution up to the convergence. Using the relation between the number of visitors and the ant population, and considering the population diversity, we develop a binary ant colony algorithm with controllable search bias. In the test of function optimization and the application to the 0-1 multiple knapsack problem, the algorithm exhibits a good search ability and a high convergence speed.
%K ant colony algorithm
%K binary ant colony algorithm
%K pheromone update pattern
%K controllable search
%K function optimization
%K 0-1 multiple knapsack problem<br>蚁群算法
%K 二元蚁群算法
%K 信息素更新方式
%K 可控搜索
%K 函数优化
%K 0-1多背包问题
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=970898A57DFC021F93AB51667BAED7F7&aid=F346442AA51D63DCADD5F3350F9914D2&yid=9377ED8094509821&vid=D3E34374A0D77D7F&iid=5D311CA918CA9A03&sid=5DD55A2029F498FE&eid=4AB97D697AC3192E&journal_id=1000-8152&journal_name=控制理论与应用&referenced_num=0&reference_num=19