Geometric Differential Evolution in MOEA/D: ...
Document type :
Communication dans un congrès avec actes
Title :
Geometric Differential Evolution in MOEA/D: A Preliminary Study
Author(s) :
Zapotecas-Martínez, Saúl [Auteur]
Faculty of Engineering [Nagano]
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Liefooghe, Arnaud [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Faculty of Engineering [Nagano]
Derbel, Bilel [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Liefooghe, Arnaud [Auteur]

Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Aguirre, Hernan [Auteur]
Faculty of Engineering [Nagano]
Tanaka, Kiyoshi [Auteur]
Faculty of Engineering [Nagano]
Scientific editor(s) :
Springer
Conference title :
The 14th LNCS-LNAI International Conference on Artificial Intelligence (MICAI)
City :
Cuernavaca
Country :
Mexique
Start date of the conference :
2015-10-25
Journal title :
The 14th LNCS-LNAI International Conference on Artificial Intelligence (MICAI)
Publisher :
Springer
Publication date :
2015-10-25
HAL domain(s) :
Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Combinatoire [math.CO]
Mathématiques [math]/Optimisation et contrôle [math.OC]
Mathématiques [math]/Combinatoire [math.CO]
Mathématiques [math]/Optimisation et contrôle [math.OC]
English abstract : [en]
The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs).So far, for the continuous ...
Show more >The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs).So far, for the continuous domain, the most successful variants of MOEA/D are based on differential evolution (DE) operators. However, no investigations on the application of DE-like operators within MOEA/D exist in the context of combinatorial optimization. This is precisely the focus of the work reported in this paper. More particularly, we study the incorporation of geometric differential evolution (gDE), the discrete generalization of DE, into the MOEA/D framework.We conduct preliminary experiments in order to study the effectiveness of gDE when coupled with MOEA/D. Our results indicate that the proposed approach is highly competitive with respect to the original version of MOEA/D, when solving a combinatorial optimization problem having between two and four objective functions.Show less >
Show more >The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is an aggregation-based algorithm which has became successful for solving multi-objective optimization problems (MOPs).So far, for the continuous domain, the most successful variants of MOEA/D are based on differential evolution (DE) operators. However, no investigations on the application of DE-like operators within MOEA/D exist in the context of combinatorial optimization. This is precisely the focus of the work reported in this paper. More particularly, we study the incorporation of geometric differential evolution (gDE), the discrete generalization of DE, into the MOEA/D framework.We conduct preliminary experiments in order to study the effectiveness of gDE when coupled with MOEA/D. Our results indicate that the proposed approach is highly competitive with respect to the original version of MOEA/D, when solving a combinatorial optimization problem having between two and four objective functions.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :