Dual Guidance in Evolutionary Multi-objective Optimization by Localization

  • Lam T. Bui
  • Kalyanmoy Deb
  • Hussein A. Abbass
  • Daryl Essam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4247)


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.


Pareto Front Local Model Multiobjective Optimization Objective Space Decision Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lam T. Bui
    • 1
  • Kalyanmoy Deb
    • 2
  • Hussein A. Abbass
    • 1
  • Daryl Essam
    • 1
  1. 1.The Artificial Life and Adaptive Robotics LaboratorySchool of ITEE, UNSW@ADFACanberraAustralia
  2. 2.Mechanical Engineering DepartmentIndian Institute of TechnologyKanpurIndia

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