Hybrid metaheuristics for multi-objective ...
Document type :
Compte-rendu et recension critique d'ouvrage
DOI :
Title :
Hybrid metaheuristics for multi-objective optimization
Author(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]
Journal title :
Journal of Algorithms and Computational Technology
Pages :
41-63
Publisher :
SAGE and Multi-science
Publication date :
2015
ISSN :
1748-3018
HAL domain(s) :
Informatique [cs]/Recherche opérationnelle [cs.RO]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
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