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 |
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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 | |
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 |
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Publisher | Springer |
Volume | 1684 CCIS |
ISSN (Print) | 1865-0929 |
ISSN (Electronic) | 1865-0937 |
Conference
Conference | International Conference on Optimization and Learning 2022 |
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Abbreviated title | OLA 2022 |
Country/Territory | Italy |
City | Syracuse |
Period | 18/07/22 → 20/07/22 |
Internet address |