A set-oriented MOEA/D
Type de document :
Communication dans un congrès avec actes
DOI :
Titre :
A set-oriented MOEA/D
Auteur(s) :
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Zhang, Qingfu [Auteur]
City University of Hong Kong [Hong Kong] [CUHK]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Zhang, Qingfu [Auteur]
City University of Hong Kong [Hong Kong] [CUHK]
Verel, Sébastien [Auteur]
Laboratoire d'Informatique Signal et Image de la Côte d'Opale [LISIC]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Éditeur(s) ou directeur(s) scientifique(s) :
Aguirre, Hernan
Takadama, Keiki
Takadama, Keiki
Titre de la manifestation scientifique :
GECCO 2018 - Genetic and Evolutionary Computation Conference
Ville :
Kyoto
Pays :
Japon
Date de début de la manifestation scientifique :
2018-07-15
Titre de l’ouvrage :
GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference
Éditeur :
ACM Press
Date de publication :
2018-07
Mot(s)-clé(s) en anglais :
multi-and many-objective optimization
decomposition
evolution-ary algorithms
combinatorial optimization
decomposition
evolution-ary algorithms
combinatorial optimization
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
The working principles of the well-established multi-objective evolutionary algorithm MOEA/D relies on the iterative and cooperative improvement of a number of single-objective sub-problems obtained by decomposition. Besides ...
Lire la suite >The working principles of the well-established multi-objective evolutionary algorithm MOEA/D relies on the iterative and cooperative improvement of a number of single-objective sub-problems obtained by decomposition. Besides the definition of sub-problems, selection and replacement are, like in any evolutionary algorithm, the two core elements of MOEA/D. We argue that these two components are however loosely coupled with the maintained population. Thereby, we propose to re-design the working principles of MOEA/D by adopting a set-oriented perspective, where a many-to-one mapping between sub-problems and solutions is considered. Selection is then performed by defining a neighborhood relation among solutions in the population set, depending on the corresponding sub-problem mapping. Replacement is performed following an elitist mechanism allowing the population to have a variable, but bounded, cardinality during the search process. By conducting a comprehensive empirical analysis on a range of combinatorial multi- and many-objective nk-landscapes, we show that the proposed approach leads to significant improvements, especially when dealing with an increasing number of objectives. Our findings indicate that a set-oriented design can constitute a sound alternative for strengthening the practice of multi- and many-objective evolutionary optimization based on decomposition.Lire moins >
Lire la suite >The working principles of the well-established multi-objective evolutionary algorithm MOEA/D relies on the iterative and cooperative improvement of a number of single-objective sub-problems obtained by decomposition. Besides the definition of sub-problems, selection and replacement are, like in any evolutionary algorithm, the two core elements of MOEA/D. We argue that these two components are however loosely coupled with the maintained population. Thereby, we propose to re-design the working principles of MOEA/D by adopting a set-oriented perspective, where a many-to-one mapping between sub-problems and solutions is considered. Selection is then performed by defining a neighborhood relation among solutions in the population set, depending on the corresponding sub-problem mapping. Replacement is performed following an elitist mechanism allowing the population to have a variable, but bounded, cardinality during the search process. By conducting a comprehensive empirical analysis on a range of combinatorial multi- and many-objective nk-landscapes, we show that the proposed approach leads to significant improvements, especially when dealing with an increasing number of objectives. Our findings indicate that a set-oriented design can constitute a sound alternative for strengthening the practice of multi- and many-objective evolutionary optimization based on decomposition.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Projet ANR :
Collections :
Source :
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