COVID-19 optimizer algorithm, modeling and controlling of coronavirus distribution process
Open accessAbstract
The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.
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Hosseini et al. developed an algorithm specifically for COVID-19 to cover nearly all feasible regions of optimization problems by modeling the mechanism of coronavirus dissemination in many countries across the globe.
“(Hosseini et al., 2020). developed an algorithm specifically for COVID-19 to cover nearly all feasible regions of optimization problems by modeling the mechanism of coronavirus dissemination in many countries across the globe.”
From IoMT amid COVID-19 pandemic: Application, architecture, technology, and security (2021) · cites this paper as Hosseini et al., 2020
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IoMT amid COVID-19 pandemic: Application, architecture, technology, and security
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- Authors
- Hosseini, E., Ghafoor, K. Z., Sadiq, A. S., Guizani, M., & Emrouznejad, A.
- Affiliations
- Erbil Polytechnic University (IQ)
- University of Wolverhampton (GB)
- Qatar University (QA)
- Aston University (GB)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2020-10
- Volume
- 24
- Issue
- 10
- Pages
- 2765-2775
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- PMC
- PMC8545158
- OpenAlex
- W3046052168
- Cited by
- 55
- References
- 29
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 7 · 2023: 12 · 2022: 14 · 2021: 12 · 2020: 5
- FWCI
- 4.23
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Wolverhampton Intellectual Repository and E-Theses (University of Wolverhampton) (acceptedVersion, cc-by-nc-nd)
- Aston Publications Explorer (Aston University) (submittedVersion)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Keywords
- COVID-19 pandemic, metaheuristic optimization, epidemic modeling, epidemic control, social distancing, NP-hard optimization, grey wolf optimizer, particle swarm optimization, genetic algorithm
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Algorithms, Computer Simulation, Humans, Models, Biological, Pneumonia, Viral, Coronavirus Infections, Computational Biology, Pandemics
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