Fractal Decomposition Approach for Continuous ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Fractal Decomposition Approach for Continuous Multi-Objective Optimization Problems
Author(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]
Journal title :
IEEE ACCESS
Pages :
167604-167619
Publisher :
IEEE
Publication date :
2020
ISSN :
2169-3536
English keyword(s) :
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
HAL domain(s) :
Informatique [cs]
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Intelligence artificielle [cs.AI]
English abstract : [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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Popular science :
Non
Collections :
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