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Weighted sum model with partial preference information: application to Multi-Objective Optimization

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Date
2017
Dewey
Recherche opérationnelle
Sujet
Multiple objective programming; Weighted sum; Partial preference information
Journal issue
European Journal of Operational Research
Volume
260
Number
2
Publication date
07-2017
Article pages
665-679
DOI
http://dx.doi.org/10.1016/j.ejor.2017.01.003
URI
https://basepub.dauphine.fr/handle/123456789/16430
Collections
  • LAMSADE : Publications
Metadata
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Author
Kaddani, Sami
251714 DCNS Research
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Vanderpooten, Daniel
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Vanpeperstraete, Jean-Michel
251714 DCNS Research
Aissi, Hassene
989 Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Type
Article accepté pour publication ou publié
Abstract (EN)
Multi-objective optimization problems often lead to large nondominated sets, as the size of the problem or the number of objectives increases. Generating the whole nondominated set requires significant computation time, while most of the corresponding solutions are irrelevant to the decision maker (DM). Optimizing an aggregation function reduces the computation time and produces one or a very limited number of more focused solutions. This requires, however, the elicitation of precise preference parameters, which is often difficult and partly arbitrary, and might discard solutions of interest. An intermediate approach consists in using partial preference information with an aggregation function. In this work, we present a preference relation based on the weighted sum aggregation, where weights are not precisely defined. We give some properties of this preference relation and define the set of preferred points as the set of nondominated points with respect to this relation. We provide an efficient and generic way of generating this preferred set using any standard multi-objective optimization algorithm. This approach shows competitive performances both on computation time and quality of the generated preferred set.

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