Indicator-based approaches for multiobjective ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Indicator-based approaches for multiobjective optimization in uncertain environments: An application to multiobjective scheduling with stochastic processing times
Author(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]
Conference title :
URPDM 2010 - 25th Mini-EURO Conference: Uncertainty and Robustness in Planning and Decision Making
City :
Coimbra
Country :
Portugal
Start date of the conference :
2010-04-15
English keyword(s) :
Multiobjective optimization problem
Uncertain environments
Evolutionary algorithms
Scheduling
Uncertain environments
Evolutionary algorithms
Scheduling
HAL domain(s) :
Informatique [cs]/Recherche opérationnelle [cs.RO]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Non spécifiée
Popular science :
Non
Collections :
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