A Local Search Based Evolutionary Multi-objective Optimization Approach for Fast and Accurate Convergence

  • Karthik Sindhya
  • Kalyanmoy Deb
  • Kaisa Miettinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)


A local search method is often introduced in an evolutionary optimization technique to enhance its speed and accuracy of convergence to true optimal solutions. In multi-objective optimization problems, the implementation of a local search is a non-trivial task, as determining a goal for the local search in presence of multiple conflicting objectives becomes a difficult proposition. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and include it as a search operator of an EMO algorithm. Simulation results with NSGA-II on a number of two to four-objective problems with and without the local search approach clearly show the importance of local search in aiding a computationally faster and more accurate convergence to Pareto-optimal solutions. The concept is now ready to be coupled with a faster and more accurate diversity-preserving procedure to make the overall procedure a competitive algorithm for multi-objective optimization.


Local Search Multiobjective Optimization Local Search Method Local Search Procedure Multiple Criterion Decision Making 
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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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Karthik Sindhya
    • 1
    • 3
  • Kalyanmoy Deb
    • 1
    • 3
  • Kaisa Miettinen
    • 2
  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KanpurIndia
  2. 2.Department of Mathematical Information TechnologyUniversity of Jyväskylä(Agora)Finland
  3. 3.Department of Business Technology, Helsinki School of EconomicsHelsinkiFinland

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