Dual guidance in evolutionary multi-objective optimization by localization

Authors:
Lam T. Bui;Kalyanmoy Deb;Hussein A. Abbass;Daryl Essam
Affiliations:
The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, UNSW@ADFA, Canberra, Australia;Mechanical Engineering Department, Indian Institute of Technology, Kanpur, India;The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, UNSW@ADFA, Canberra, Australia;The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, UNSW@ADFA, Canberra, Australia
Venue:
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Year:
2006

Citing 6
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Abstract

In this paper, we propose a framework using local models for multi-objective optimization to guide the search heuristic in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres 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 using the guided dominance technique in the objective space. With this dual guidance, we can easily guide spheres towards different parts of the Pareto front while also exploring the decision space efficiently.