Fractal Decomposition Approach for Continuous ...
Type de document :
Compte-rendu et recension critique d'ouvrage
Titre :
Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems
Auteur(s) :
Souquet, Leo [Auteur]
Data ScienceTech Institute [DSTI Labs]
Talbi, El Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Nakib, Amir [Auteur]
Laboratoire Images, Signaux et Systèmes Intelligents [LISSI]
Data ScienceTech Institute [DSTI Labs]
Talbi, El Ghazali [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Nakib, Amir [Auteur]
Laboratoire Images, Signaux et Systèmes Intelligents [LISSI]
Titre de la revue :
IEEE ACCESS
Pagination :
167604-167619
Éditeur :
IEEE
Date de publication :
2020
ISSN :
2169-3536
Mot(s)-clé(s) en anglais :
Multi-Objective optimization
Large-scale optimization
Metaheuristics
Geometric Fractal Decomposition
Local Search
Continuous optimization
Containers
Virtualization
Docker
Kubernetes
Large-scale optimization
Metaheuristics
Geometric Fractal Decomposition
Local Search
Continuous optimization
Containers
Virtualization
Docker
Kubernetes
Discipline(s) HAL :
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Intelligence artificielle [cs.AI]
Résumé en anglais : [en]
Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key ...
Lire la suite >Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decisionspace using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA’s properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.Lire moins >
Lire la suite >Multi-objective optimization problems (MOPs) have been widely studied during the last decades. In this paper, we present a new intrinsically parallel approach based on Fractal decomposition (FDA) to solve MOPs. The key contribution of the proposed approach is to divide recursively the decisionspace using hyperspheres. Two different methods were investigated: the first one is based on scalarization that has been distributed on a parallel multi-node architecture virtual environments and taking profit from the FDA’s properties, while the second method is based on Pareto dominance sorting. A comparison with state of the art algorithms on different well known benchmarks shows the efficiency and the robustness of the proposed decomposition approaches.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
Collections :
Source :
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