Indicator-based approaches for multiobjective ...
Type de document :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Titre :
Indicator-based approaches for multiobjective optimization in uncertain environments: An application to multiobjective scheduling with stochastic processing times
Auteur(s) :
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Titre de la manifestation scientifique :
URPDM 2010 - 25th Mini-EURO Conference: Uncertainty and Robustness in Planning and Decision Making
Ville :
Coimbra
Pays :
Portugal
Date de début de la manifestation scientifique :
2010-04-15
Mot(s)-clé(s) en anglais :
Multiobjective optimization problem
Uncertain environments
Evolutionary algorithms
Scheduling
Uncertain environments
Evolutionary algorithms
Scheduling
Discipline(s) HAL :
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
Many real-world optimization problems have to face a lot of difficulties: they are often characterized by large and complex search spaces, multiple conflicting objective functions, and a host of uncertainties that have to ...
Lire la suite >Many real-world optimization problems have to face a lot of difficulties: they are often characterized by large and complex search spaces, multiple conflicting objective functions, and a host of uncertainties that have to be taken into account. Metaheuristics are natural candidates to solve those problems and make them preferable to classical optimization methods. We here propose a number of new evolutionary algorithms to find a set of non-dominated solutions from multiobjective optimization problems in uncertain environments. Experiments are conducted on multiobjective scheduling with stochastic processing times.Lire moins >
Lire la suite >Many real-world optimization problems have to face a lot of difficulties: they are often characterized by large and complex search spaces, multiple conflicting objective functions, and a host of uncertainties that have to be taken into account. Metaheuristics are natural candidates to solve those problems and make them preferable to classical optimization methods. We here propose a number of new evolutionary algorithms to find a set of non-dominated solutions from multiobjective optimization problems in uncertain environments. Experiments are conducted on multiobjective scheduling with stochastic processing times.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Non spécifiée
Vulgarisation :
Non
Collections :
Source :
Fichiers
- document
- Accès libre
- Accéder au document
- liefooghe_urpdm2010.pdf
- Accès libre
- Accéder au document