Hybrid metaheuristics for multi-objective ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
Hybrid metaheuristics for multi-objective optimization
Auteur(s) :
Talbi, El-Ghazali [Auteur]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la revue :
Journal of Algorithms and Computational Technology
Pagination :
41-63
Éditeur :
SAGE and Multi-science
Date de publication :
2015
ISSN :
1748-3018
Discipline(s) HAL :
Informatique [cs]/Recherche opérationnelle [cs.RO]
Résumé en anglais : [en]
Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization ...
Lire la suite >Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming and machine learning techniques have provided very powerful search algorithms. Three different types of combinations are considered in this paper to solve multi-objective optimization problems:Combining metaheuristics with (complementary) metaheuristics.Combining metaheuristics with exact methods from mathematical programming approaches.Combining metaheuristics with machine learning and data mining techniques.Lire moins >
Lire la suite >Over the last two decades, interest on hybrid metaheuristics has risen considerably in the field of multi-objective optimization (MOP). The best results found for many real-life or academic multi-objective optimization problems are obtained by hybrid algorithms. Combinations of algorithms such as metaheuristics, mathematical programming and machine learning techniques have provided very powerful search algorithms. Three different types of combinations are considered in this paper to solve multi-objective optimization problems:Combining metaheuristics with (complementary) metaheuristics.Combining metaheuristics with exact methods from mathematical programming approaches.Combining metaheuristics with machine learning and data mining techniques.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :