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COVID-19 optimizer algorithm, modeling and controlling of coronavirus distribution process
Hosseini, Eghbal Ghafoor, Kayhan Zrar Sadiq, Ali Safaa Guizani, Mohsen Emrouznejad, Ali
Hosseini, Eghbal
Ghafoor, Kayhan Zrar
Sadiq, Ali Safaa
Guizani, Mohsen
Emrouznejad, Ali
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2020-07-28
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Abstract
The emergence of novel COVID-19 causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and prevent the infection rate.It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. Further, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems.This paper first proposes a novel COVID19 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 henceslow 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 outperform the others.Extensive simulations using several optimization problems 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, E., Ghafoor, K.Z., Sadiq, A.S., Guizani, M. and Emrouznejad, A. (2020) COVID-19 optimizer algorithm, modeling and controlling of coronavirus distribution process, IEEE Journal of Biomedical and Health Informatics, 24 (10), pp. 2765-2775 DOI 10.1109/JBHI.2020.3012487
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Journal article
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en
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This is an accepted manuscript of an article published by IEEE in IEEE Journal of Biomedical and Health Informatics on 28/07/2020, available online: https://ieeexplore.ieee.org/document/9151280
The accepted version of the publication may differ from the final published version.
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2168-2194