Compromise Based Evolutionary Multiobjective Optimization Algorithm for Multidisciplinary Optimization

  • Benoît Guédas
  • Xavier Gandibleux
  • Philippe Dépincé
Conference paper

DOI: 10.1007/978-3-642-19695-9_6

Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 648)
Cite this paper as:
Guédas B., Gandibleux X., Dépincé P. (2011) Compromise Based Evolutionary Multiobjective Optimization Algorithm for Multidisciplinary Optimization. In: Shi Y., Wang S., Kou G., Wallenius J. (eds) New State of MCDM in the 21st Century. Lecture Notes in Economics and Mathematical Systems, vol 648. Springer, Berlin, Heidelberg

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.

Keywords

Compromise solutions Evolutionary algorithm Multidisciplinary optimization Multiobjective optimization Preferences. 

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Benoît Guédas
    • 1
  • Xavier Gandibleux
    • 2
  • Philippe Dépincé
    • 1
  1. 1.IRCCyNÉcole Centrale de NantesNantes Cedex 03France
  2. 2.LINAUniversité de NantesNantes Cedex 03France

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