Journal of Computer Science and Technology

, Volume 23, Issue 1, pp 44–63 | Cite as

Interleaving Guidance in Evolutionary Multi-Objective Optimization

  • Lam Thu BuiEmail author
  • Kalyanmoy Deb
  • Hussein A. Abbass
  • Daryl Essam
Regular Paper


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.


evolutionary multi-objective optimization guided dominance local models 


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Copyright information

© Science Press, Beijing, China and Springer Science + Business Media, LLC, USA 2008

Authors and Affiliations

  • Lam Thu Bui
    • 1
    Email author
  • Kalyanmoy Deb
    • 2
  • Hussein A. Abbass
    • 1
  • Daryl Essam
    • 1
  1. 1.The Artificial Life and Adaptive Robotics Laboratory, School of ITEE, ADFAUniversity of New South WalesCanberraAustralia
  2. 2.Mechanical Engineering DepartmentIndian Institute of TechnologyKanpurIndia

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