Interleaving Guidance in Evolutionary Multi-Objective Optimization

作     者：Lam Thu Bui Kalyanmoy Deb Hussein A.Abbass Daryl Essam 

作者机构：The Artificial Life and Adaptive Robotics LaboratorySchool of ITEEADFAUniversity of New South Wales Canberra Mechanical Engineering DepartmentIndian Institute of TechnologyKanpur 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2008年第23卷第1期

页      面：44-63页

核心收录：

学科分类：0808[工学-电气工程] 080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0835[工学-软件工程] 0802[工学-机械工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work is supported by the Australian Research Council(ARC)Centre for Complex Systems under Grant No.CEO0348249 the Postgraduate Research Student Overseas Grant from UNSW@ADFA,University of New South Wales 

主　　题：evolutionary multi-objective optimization guided dominance local models 

摘      要：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 axe 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 Paxeto 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 Paxeto 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.