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Interleaving Guidance in Evolutionary Multi-Objective Optimization

Lam Thu Bui1, Kalyanmoy Deb2, Hussein A. Abbass1, and Daryl Essam1   

  1. 1The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, ADFA, University of New South Wales Canberra, ACT, 2600, Australia 2Mechanical Engineering Department, Indian Institute of Technology, Kanpur, PIN 208 016, India
  • Revised:2007-11-19 Online:2008-01-15 Published:2008-01-10

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.

Key words: communication protocol; self-stabilization; global state; recoveryalgorithm; multimedia communication;



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