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|a 10.1007/s10462-021-10052-w
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|a eng
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1 |
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|a Piotrowski, Adam P
|e verfasserin
|4 aut
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|a Differential evolution and particle swarm optimization against COVID-19
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|c 2022
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|a Text
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|a ƒaComputermedien
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|a Date Revised 28.08.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © The Author(s) 2021.
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|a COVID-19 disease, which highly affected global life in 2020, led to a rapid scientific response. Versatile optimization methods found their application in scientific studies related to COVID-19 pandemic. Differential Evolution (DE) and Particle Swarm Optimization (PSO) are two metaheuristics that for over two decades have been widely researched and used in various fields of science. In this paper a survey of DE and PSO applications for problems related with COVID-19 pandemic that were rapidly published in 2020 is presented from two different points of view: 1. practitioners seeking the appropriate method to solve particular problem, 2. experts in metaheuristics that are interested in methodological details, inter comparisons between different methods, and the ways for improvement. The effectiveness and popularity of DE and PSO is analyzed in the context of other metaheuristics used against COVID-19. It is found that in COVID-19 related studies: 1. DE and PSO are most frequently used for calibration of epidemiological models and image-based classification of patients or symptoms, but applications are versatile, even interconnecting the pandemic and humanities; 2. reporting on DE or PSO methodological details is often scarce, and the choices made are not necessarily appropriate for the particular algorithm or problem; 3. mainly the basic variants of DE and PSO that were proposed in the late XX century are applied, and research performed in recent two decades is rather ignored; 4. the number of citations and the availability of codes in various programming languages seems to be the main factors for choosing metaheuristics that are finally used
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|a Journal Article
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|a Applications
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|a COVID-19
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|a Differential evolution
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| 650 |
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|a Evolutionary computation
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| 650 |
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|a Particle swarm optimization
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| 650 |
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|a Swarm intelligence
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| 700 |
1 |
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|a Piotrowska, Agnieszka E
|e verfasserin
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|i Enthalten in
|t Artificial intelligence review
|d 1998
|g 55(2022), 3 vom: 23., Seite 2149-2219
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|g volume:55
|g year:2022
|g number:3
|g day:23
|g pages:2149-2219
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