%0 Journal Article
%T Dynamic-Window-Search Ant Colony Optimization for Complex Multi-Stage Decision Making Problems<br>求解复杂多阶段决策问题的动态窗口蚁群优化算法
%A WEN Yu
%A WU Tie-Jun
%A <br>闻育
%A 吴铁军
%J 自动化学报
%D 2004
%I 
%X A dynamic-window-search ant colony optimization(ACO)algorithm,integratedwith genetic optimization techniques,is proposed for large-scale multi-stage decision mak-ing problems,which are of strong nonlinearity,complex constraints on system states andcontrol inputs,non-analytical system representation,and additive and monotonic objectivefunctions.A subset of the feasible decision set at each stage is dynamically selected for the al-gorithm by real-coded genetic optimization and is mapped to the nodes of the correspondinglayer in a layered construction graph to reduce the size of the search space.Computationalcomplexity analysis and simulation results demonstrate that,in comparison with basic ACOalgorithms,the proposed algorithm greatly improves the computational efficiency.
%K Multi-stage decision making problem
%K ant colony optimization
%K complex systems optimization<br>多阶段决策
%K 蚁群算法
%K 复杂系统优化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=2E2796697B93FC0A&yid=D0E58B75BFD8E51C&vid=340AC2BF8E7AB4FD&iid=B31275AF3241DB2D&sid=11632AEF1E1F2092&eid=D698D0190A84C2BD&journal_id=0254-4156&journal_name=自动化学报&referenced_num=4&reference_num=7