Multi-environmental cooperative parallel ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Multi-environmental cooperative parallel metaheuristics for solving dynamic optimization problems
Auteur(s) :
Redouane, Mostepha [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Sarasola, Briseida [Auteur]
Alba, Enrique [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Jourdan, Laetitia [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Sarasola, Briseida [Auteur]
Alba, Enrique [Auteur]
Titre de la revue :
Journal of Supercomputing
Pagination :
836-853
Éditeur :
Springer Verlag
Date de publication :
2013
ISSN :
0920-8542
Mot(s)-clé(s) en anglais :
Dynamic optimization problems
Multi-environmental cooperative model
Integration policies
Multi-swarm
Dynamic vehicle routing problem
Multi-environmental cooperative model
Integration policies
Multi-swarm
Dynamic vehicle routing problem
Discipline(s) HAL :
Mathématiques [math]/Combinatoire [math.CO]
Résumé en anglais : [en]
In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered ...
Lire la suite >In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.Lire moins >
Lire la suite >In dynamic optimization problems, changes occur over time. These changes could be related to the optimization objective, the problem instance, or involve problem constraints. In most cases, they are seen as an ordered sequence of sub-problems or environments that must be solved during a certain time interval. The usual approaches tend to solve each sub-problem when a change happens, dealing always with one single environment at each time instant. In this paper, we propose a multi-environmental cooperative model for parallel meta-heuristics to tackle dynamic optimization problems. It consists in dealing with different environments at the same time, using different algorithms that exchange information coming from these environments. A parallel multi-swarm approach is presented for solving the Dynamic Vehicle Routing Problem. The effectiveness of the proposed approach is tested on a well-known set of benchmarks, and compared with other meta-heuristics from the literature. Experimental results show that our multi-environmental approach outperforms conventional meta-heuristics on this problem.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
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