The efficiency of indicator-based local ...
Type de document :
Article dans une revue scientifique
Titre :
The efficiency of indicator-based local search for multi-objective combinatorial optimisation problems
Auteur(s) :
Basseur, Matthieu [Auteur correspondant]
Laboratoire d'Etudes et de Recherche en Informatique d'Angers [LERIA]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Khoi, Le [Auteur]
Burke, Edmund [Auteur]
Laboratoire d'Etudes et de Recherche en Informatique d'Angers [LERIA]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Khoi, Le [Auteur]
Burke, Edmund [Auteur]
Titre de la revue :
Journal of Heuristics
Pagination :
263-296
Éditeur :
Springer Verlag
Date de publication :
2012-04
ISSN :
1381-1231
Mot(s)-clé(s) en anglais :
Multi-objective optimisation
Metaheuristic
Local search
Indicator-based optimisation
Flow-shop problem
Ring star problem
Nurse scheduling problem
Metaheuristic
Local search
Indicator-based optimisation
Flow-shop problem
Ring star problem
Nurse scheduling problem
Discipline(s) HAL :
Informatique [cs]/Recherche opérationnelle [cs.RO]
Résumé en anglais : [en]
In the last few years, a significant number of multi-objective metaheuristics have been proposed in the literature in order to address real-world problems. Local search methods play a major role in many of these metaheuristic ...
Lire la suite >In the last few years, a significant number of multi-objective metaheuristics have been proposed in the literature in order to address real-world problems. Local search methods play a major role in many of these metaheuristic procedures. In this paper, we adapt a recent and popular indicator-based selection method proposed by Zitzler and Künzli in 2004, in order to define a population-based multi-objective local search. The proposed algorithm is designed in order to be easily adaptable, parameter independent and to have a high convergence rate. In order to evaluate the capacity of our algorithm to reach these goals, a large part of the paper is dedicated to experiments. Three combinatorial optimisation problems are tested: a flow shop problem, a ring star problem and a nurse scheduling problem. The experiments show that our algorithm can be applied with success to different types of multi-objective optimisation problems and that it outperforms some classical metaheuristics. Furthermore, the parameter sensitivity analysis enables us to provide some useful guidelines about how to set the parameters.Lire moins >
Lire la suite >In the last few years, a significant number of multi-objective metaheuristics have been proposed in the literature in order to address real-world problems. Local search methods play a major role in many of these metaheuristic procedures. In this paper, we adapt a recent and popular indicator-based selection method proposed by Zitzler and Künzli in 2004, in order to define a population-based multi-objective local search. The proposed algorithm is designed in order to be easily adaptable, parameter independent and to have a high convergence rate. In order to evaluate the capacity of our algorithm to reach these goals, a large part of the paper is dedicated to experiments. Three combinatorial optimisation problems are tested: a flow shop problem, a ring star problem and a nurse scheduling problem. The experiments show that our algorithm can be applied with success to different types of multi-objective optimisation problems and that it outperforms some classical metaheuristics. Furthermore, the parameter sensitivity analysis enables us to provide some useful guidelines about how to set the parameters.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
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