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Interleaving Guidance in Evolutionary Multi-Objective Optimization

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Abstract

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.

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Authors and Affiliations

  1. The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, ADFA, University of New South Wales, Canberra, ACT, 2600, Australia

    Lam Thu Bui, Hussein A. Abbass & Daryl Essam

  2. Mechanical Engineering Department, Indian Institute of Technology, Kanpur, PIN 208 016, India

    Kalyanmoy Deb

Authors
  1. Lam Thu Bui
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  2. Kalyanmoy Deb
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  3. Hussein A. Abbass
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  4. Daryl Essam
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Corresponding author

Correspondence to Lam Thu Bui.

Additional information

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.

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Bui, L.T., Deb, K., Abbass, H.A. et al. Interleaving Guidance in Evolutionary Multi-Objective Optimization. J. Comput. Sci. Technol. 23, 44–63 (2008). https://doi.org/10.1007/s11390-008-9114-2

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