Titre :

An approach fort he local exploration of discrete many objective optimization problems

Auteur(s) :

Cuate, Oliver [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Schütze, Oliver [Auteur]
Centro de Investigacion y de Estudios Avanzados del Instituto Politécnico Nacional [CINVESTAV]

Titre de la manifestation scientifique :

EMO 2017 - 9th International Conference on Evolutionary Multi-Criterion Optimization

Ville :

Münster

Pays :

Allemagne

Date de début de la manifestation scientifique :

2017-03-19

Titre de l’ouvrage :

EMO 2017: Evolutionary Multi-Criterion Optimization

Titre de la revue :

LNCS

Date de publication :

2017

Mot(s)-clé(s) en anglais :

Many objective optimization
Multi-criteria decision making
Discrete optimization
Knapsack
Evolutionary computation

Discipline(s) HAL :

Informatique [cs]
Computer Science [cs]/Operations Research [math.OC]

Résumé en anglais : [en]

Multi-objective optimization problems with more than three objectives, which are also termed as many objective optimization problems, play an important role in the decision making process. For such problems, it is ...
Lire la suite >Multi-objective optimization problems with more than three objectives, which are also termed as many objective optimization problems, play an important role in the decision making process. For such problems, it is computationally expensive or even intractable to approximate the entire set of optimal solutions. An alternative is to compute a subset of optimal solutions based on the preferences of the decision maker. Commonly, interactive methods from the literature consider the user preferences at every iteration by means of weight vectors or reference points. Besides the fact that mathematical programming techniques only produce one solution at each iteration, they generally require first or second derivative information, that limits its applicability to certain problems. The approach proposed in this paper allows to steer the search into any direction in the objective space for optimization problems of discrete nature. This provides a more intuitive way to set the preferences, which represents a useful tool to explore the regions of interest of the decision maker. Numerical results on multi-objective multi-dimensional knapsack problem instances show the interest of the proposed approach.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL