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

doi:10.1007/978-3-031-22039-5_16

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

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Optimization and Learning
Subtitle of host publication	5th International Conference, OLA 2022, Proceedings
Editors	Bernabé Dorronsoro, Mario Pavone, Amir Nakib, El-Ghazali Talbi
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	201-212
Number of pages	12
ISBN (Electronic)	9783031220395
ISBN (Print)	9783031220388
DOIs	https://doi.org/10.1007/978-3-031-22039-5_16
Publication status	Published - 2022
Event	International Conference on Optimization and Learning 2022 - Syracuse, Italy Duration: 18 Jul 2022 → 20 Jul 2022 Conference number: 5th https://ola2022.sciencesconf.org/

Publication series

Name	Communications in Computer and Information Science
Publisher	Springer
Volume	1684 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	International Conference on Optimization and Learning 2022
Abbreviated title	OLA 2022
Country/Territory	Italy
City	Syracuse
Period	18/07/22 → 20/07/22
Internet address	https://ola2022.sciencesconf.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-22039-5_16

Cite this

Briffoteaux, G., Ragonnet, R., Tomenko, P., Mezmaz, M., Melab, N., & Tuyttens, D. (2022). Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation. In B. Dorronsoro, M. Pavone, A. Nakib, & E.-G. Talbi (Eds.), Optimization and Learning: 5th International Conference, OLA 2022, Proceedings (pp. 201-212). (Communications in Computer and Information Science; Vol. 1684 CCIS). Springer. https://doi.org/10.1007/978-3-031-22039-5_16

Briffoteaux, Guillaume ; Ragonnet, Romain ; Tomenko, Pierre et al. / Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation. Optimization and Learning: 5th International Conference, OLA 2022, Proceedings. editor / Bernabé Dorronsoro ; Mario Pavone ; Amir Nakib ; El-Ghazali Talbi. Cham Switzerland : Springer, 2022. pp. 201-212 (Communications in Computer and Information Science).

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abstract = "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.",

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Briffoteaux, G, Ragonnet, R, Tomenko, P, Mezmaz, M, Melab, N & Tuyttens, D 2022, Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation. in B Dorronsoro, M Pavone, A Nakib & E-G Talbi (eds), Optimization and Learning: 5th International Conference, OLA 2022, Proceedings. Communications in Computer and Information Science, vol. 1684 CCIS, Springer, Cham Switzerland, pp. 201-212, International Conference on Optimization and Learning 2022, Syracuse, Italy, 18/07/22. https://doi.org/10.1007/978-3-031-22039-5_16

Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation. / Briffoteaux, Guillaume; Ragonnet, Romain; Tomenko, Pierre et al.
Optimization and Learning: 5th International Conference, OLA 2022, Proceedings. ed. / Bernabé Dorronsoro; Mario Pavone; Amir Nakib; El-Ghazali Talbi. Cham Switzerland: Springer, 2022. p. 201-212 (Communications in Computer and Information Science; Vol. 1684 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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Briffoteaux G, Ragonnet R, Tomenko P, Mezmaz M, Melab N, Tuyttens D. Comparing Parallel Surrogate-Based and Surrogate-Free Multi-objective Optimization of COVID-19 Vaccines Allocation. In Dorronsoro B, Pavone M, Nakib A, Talbi EG, editors, Optimization and Learning: 5th International Conference, OLA 2022, Proceedings. Cham Switzerland: Springer. 2022. p. 201-212. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-22039-5_16