Titre :

On Maintaining Diversity in MOEA/D: Application to a Biobjective Combinatorial FJSP

Auteur(s) :

Juan, José [Auteur]
Universidad de Oviedo = University of Oviedo
Derbel, Bilel [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]

Titre de la manifestation scientifique :

GECCO '15 - Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Ville :

Madrid

Pays :

Espagne

Date de début de la manifestation scientifique :

2015-07-11

Titre de la revue :

The 24th ACM Genetic and Evolutionary Computation Conference (GECCO)

Mot(s)-clé(s) en anglais :

multiobjective combinatorial optimization
Fuzzy
Jobshop problem
decomposition based evolutionary algorithm

Discipline(s) HAL :

Computer Science [cs]/Operations Research [math.OC]
Mathématiques [math]/Combinatoire [math.CO]
Mathématiques [math]/Optimisation et contrôle [math.OC]

Résumé en anglais : [en]

MOEA/D is a generic decomposition-based multiobjective optimization framework which has been proved to be extremely effective in solving a broad range of optimization problems especially for continuous domains. In this ...
Lire la suite >MOEA/D is a generic decomposition-based multiobjective optimization framework which has been proved to be extremely effective in solving a broad range of optimization problems especially for continuous domains. In this paper, we consider applying MOEA/D to solve a bi-objective scheduling combinatorial problem in which task durations and due-dates are uncertain. Surprisingly, we find that the conventional MOEA/D implementation provides poor performance in our application setting. We show that this is because the replacement strategy underlying MOEA/D is suffering some shortcomes that lead to low population diversity, and thus to premature convergence. Consequently, we investigate existing variants of MOEA/D and we propose a novel and simple alternative replacement component at the aim of maintaining population diversity. Through extensive experiments, we then provide a comprehensive analysis on the relative performance and the behavior of the considered algorithms. Besides being able to outperform existing MOEA/D variants, as well as the standard NSGA-II algorithm, our investigations provide new insights into the search ability of MOEA/D and highlight new research opportunities for improving its design components.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL