A Parallel Multiple Reference Point Approach ...
Type de document :
Rapport de recherche
Titre :
A Parallel Multiple Reference Point Approach for Multi-objective Optimization
Auteur(s) :
Figueira, José [Auteur]
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Center for Management Studies, Instituto Superior Técnico [Porto Salvo] [CEG - IST]
Liefooghe, Arnaud [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Wierzbicki, Andrzej [Auteur]
National Institute of Telecommunications [NIT]
Japan Advanced Institute of Science and Technology [JAIST]
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Center for Management Studies, Instituto Superior Técnico [Porto Salvo] [CEG - IST]
Liefooghe, Arnaud [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Wierzbicki, Andrzej [Auteur]
National Institute of Telecommunications [NIT]
Japan Advanced Institute of Science and Technology [JAIST]
Institution :
INRIA
Date de publication :
2009
Mot(s)-clé(s) en anglais :
multi-objective optimization
reference point
achievement scalarizing function
parallelization
bi-objective scheduling problems
reference point
achievement scalarizing function
parallelization
bi-objective scheduling problems
Discipline(s) HAL :
Mathématiques [math]/Combinatoire [math.CO]
Résumé en anglais : [en]
This document presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead ...
Lire la suite >This document presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper.Lire moins >
Lire la suite >This document presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper.Lire moins >
Langue :
Anglais
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