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.
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Notes
- 1.
Ordered sets which are not necessarily antisymmetric.
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Acknowledgements
The authors would like to thank the regional council of the Pays de la Loire (France), MILES project, for their support of this research.
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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. https://doi.org/10.1007/978-3-642-19695-9_6
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DOI: https://doi.org/10.1007/978-3-642-19695-9_6
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