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Compromise Based Evolutionary Multiobjective Optimization Algorithm for Multidisciplinary Optimization

In: New State of MCDM in the 21st Century

Author

Listed:
  • Benoît Guédas

    (École Centrale de Nantes)

  • Xavier Gandibleux

    (Université de Nantes)

  • Philippe Dépincé

    (École Centrale de Nantes)

Abstract

Multidisciplinary Design Optimization deals with engineering problems composed of several sub-problems – called disciplines – that can have antagonist goals and thus require to find compromise solutions. Moreover, the sub-problems are often multiobjective optimization problems. In this case, the compromise solutions between the disciplines are often considered as compromises between all objectives of the problem, which may be not relevant in this context. We propose two alternative definitions of the compromise between disciplines. Their implementations within the well-known NSGA-II algorithm are studied and results are discussed.

Suggested Citation

  • Benoît Guédas & Xavier Gandibleux & Philippe Dépincé, 2011. "Compromise Based Evolutionary Multiobjective Optimization Algorithm for Multidisciplinary Optimization," Lecture Notes in Economics and Mathematical Systems, in: Yong Shi & Shouyang Wang & Gang Kou & Jyrki Wallenius (ed.), New State of MCDM in the 21st Century, chapter 0, pages 69-78, Springer.
    • Handle: RePEc:spr:lnechp:978-3-642-19695-9_6
    DOI: 10.1007/978-3-642-19695-9_6
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