Efficient Multi-Objective Optimization Using 2-Population Cooperative Coevolution

  • Alexandru-Ciprian Zăvoianu
  • Edwin Lughofer
  • Wolfgang Amrhein
  • Erich Peter Klement
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8111)


We propose a 2-population cooperative coevolutionary optimization method that can efficiently solve multi-objective optimization problems as it successfully combines positive traits from classic multi-objective evolutionary algorithms and from newer optimization approaches that explore the concept of differential evolution. A key part of the algorithm lies in the proposed dual fitness sharing mechanism that is able to smoothly transfer information between the two coevolved populations without negatively impacting the independent evolutionary process behavior that characterizes each population.


continuous multi-objective optimization evolutionary algorithms cooperative coevolution differential evolution 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Alexandru-Ciprian Zăvoianu
    • 1
    • 3
  • Edwin Lughofer
    • 1
  • Wolfgang Amrhein
    • 2
    • 3
  • Erich Peter Klement
    • 1
    • 3
  1. 1.Department of Knowledge-based Mathematical Systems/Fuzzy Logic LaboratoryJohannes Kepler University of LinzLinz-HagenbergAustria
  2. 2.Institute for Electrical Drives and Power ElectronicsJohannes Kepler University of LinzAustria
  3. 3.ACCM, Austrian Center of Competence in MechatronicsLinzAustria

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