%0 Journal Article
%T Interleaving Guidance in Evolutionary Multi-Objective Optimization
%A Lam Thu Bui Kalyanmoy Deb Hussein AAbbass Daryl Essam The Artificial Life
%A Adaptive Robotics Laboratory
%A School of ITEE
%A ADFA
%A University of New South Wales Canberra
%A ACT
%A Australia Mechanical Engineering
%A <br>Lam Thu Bui
%A Kalyanmoy Deb
%A Hussein A.Abbass
%A Daryl Essam
%J 计算机科学技术学报
%D 2008
%I 
%X In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Pareto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version. This work is supported by the Australian Research Council (ARC) Centre for Complex Systems under Grant No. CEO0348249 and the Postgraduate Research Student Overseas Grant from UNSW@ADFA, University of New South Wales.
%K evolutionary multi-objective optimization
%K guided dominance
%K local models<br>进化算法
%K 多目标算法
%K 优势
%K 局部模型
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=992A5F12EDB29A652A2FCE811D3DD042&yid=67289AFF6305E306&vid=EA389574707BDED3&iid=CA4FD0336C81A37A&sid=1AE5323881A5ECDC&eid=E84BBBDDD74F497C&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=0&reference_num=22