Natural Computing

, Volume 10, Issue 4, pp 1407–1430 | Cite as

Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm



A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multi-objective optimization problems, the implementation of local search is a non-trivial task, as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task. 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 integrate it as a search operator with a concurrent approach in an evolutionary multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective problems compared to a serial approach, clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front.


Multicriteria optimization Multiple criteria decision making Pareto optimality Evolutionary algorithms Hybrid algorithms Achievement scalarizing functions NSGA-II 



The support provided by the Academy of Finland (grant 118319) and the Jenny and Antti Wihuri foundation is highly appreciated.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Karthik Sindhya
    • 1
  • Kalyanmoy Deb
    • 2
    • 3
  • Kaisa Miettinen
    • 1
  1. 1.Department of Mathematical Information TechnologyUniversity of JyväskyläFinland
  2. 2.Aalto University School of Economics, Department of Business TechnologyAaltoFinland
  3. 3.Department of Mechanical EngineeringIndian Institute of Technology KanpurKanpurIndia

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