Interleaving Guidance in Evolutionary Multi-Objective Optimization  

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

机构地区：[1]The Artificial Life and Adaptive Robotics Laboratory,School of ITEE,ADFA,University of New South Wales Canberra [2]Mechanical Engineering Department,Indian Institute of Technology,Kanpur

出　　处：《Journal of Computer Science & Technology》2008年第1期44-63,共20页计算机科学技术学报（英文版）

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.

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.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.

关 键 词：evolutionary multi-objective optimization guided dominance local models 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]