Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation

Guillaume Briffoteaux, Romain Ragonnet, Pierre Tomenko, Mohand Mezmaz, Nouredine Melab, Daniel Tuyttens

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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.

Original languageEnglish
Title of host publicationOptimization and Learning
Subtitle of host publication5th International Conference, OLA 2022, Proceedings
EditorsBernabé Dorronsoro, Mario Pavone, Amir Nakib, El-Ghazali Talbi
Place of PublicationCham Switzerland
Number of pages12
ISBN (Electronic)9783031220395
ISBN (Print)9783031220388
Publication statusPublished - 2022
EventInternational Conference on Optimization and Learning 2022 - Syracuse, Italy
Duration: 18 Jul 202220 Jul 2022
Conference number: 5th

Publication series

NameCommunications in Computer and Information Science
Volume1684 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceInternational Conference on Optimization and Learning 2022
Abbreviated titleOLA 2022
Internet address

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