Comparing Parallel Surrogate-based and ...
Type de document :
Communication dans un congrès avec actes
Titre :
Comparing Parallel Surrogate-based and Surrogate-free Multi-Objective Optimization of COVID-19 vaccines allocation
Auteur(s) :
Briffoteaux, Guillaume [Auteur]
Université de Mons / University of Mons [UMONS]
Optimisation de grande taille et calcul large échelle [BONUS]
Ragonnet, Romain [Auteur]
Monash University [Melbourne]
Tomenko, Pierre [Auteur]
Université de Mons / University of Mons [UMONS]
Mezmaz, Mohand [Auteur]
Université de Mons / University of Mons [UMONS]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Tuyttens, Daniel [Auteur]
Université de Mons / University of Mons [UMONS]
Université de Mons / University of Mons [UMONS]
Optimisation de grande taille et calcul large échelle [BONUS]
Ragonnet, Romain [Auteur]
Monash University [Melbourne]
Tomenko, Pierre [Auteur]
Université de Mons / University of Mons [UMONS]
Mezmaz, Mohand [Auteur]
Université de Mons / University of Mons [UMONS]
Melab, Nouredine [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Tuyttens, Daniel [Auteur]
Université de Mons / University of Mons [UMONS]
Titre de la manifestation scientifique :
OLA 2022 - International Conference on Optimization and Learning
Ville :
Syracuse
Pays :
Italie
Date de début de la manifestation scientifique :
2022-07-18
Discipline(s) HAL :
Mathématiques [math]/Optimisation et contrôle [math.OC]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Modélisation et simulation
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Modélisation et simulation
Résumé en anglais : [en]
The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak ...
Lire la suite >The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem.Lire moins >
Lire la suite >The simulation-based and computationally expensive problem tackled in this paper addresses COVID-19 vaccines allocation in Malaysia. The multi-objective formulation considers simultaneously the total number of deaths, peak hospital occupancy and relaxation of mobility restrictions. Evolutionary algorithms have proven their capability to handle multi-to-many objectives but require a high number of computationally expensive simulations. The available techniques to raise the challenge rely on the joint use of surrogate-assisted optimization and parallel computing to deal with computational expensiveness. On the one hand, the simulation software is imitated by a cheap-to-evaluate surrogate model. On the other hand, multiple candidates are simultaneously assessed via multiple processing cores. In this study, we compare the performance of recently proposed surrogate-free and surrogate-based parallel multi-objective algorithms through the application to the COVID-19 vaccine distribution problem.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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