Gradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic

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dc.contributor.authorKhalilpourazari S.
dc.contributor.authorHashemi Doulabi H.
dc.contributor.authorÖzyüksel Çiftçioğlu A.
dc.contributor.authorWeber G.-W.
dc.date.accessioned2024-07-22T08:05:37Z
dc.date.available2024-07-22T08:05:37Z
dc.date.issued2021
dc.description.abstractThis research proposes a new type of Grey Wolf optimizer named Gradient-based Grey Wolf Optimizer (GGWO). Using gradient information, we accelerated the convergence of the algorithm that enables us to solve well-known complex benchmark functions optimally for the first time in this field. We also used the Gaussian walk and Lévy flight to improve the exploration and exploitation capabilities of the GGWO to avoid trapping in local optima. We apply the suggested method to several benchmark functions to show its efficiency. The outcomes reveal that our algorithm performs superior to most existing algorithms in the literature in most benchmarks. Moreover, we apply our algorithm for predicting the COVID-19 pandemic in the US. Since the prediction of the epidemic is a complicated task due to its stochastic nature, presenting efficient methods to solve the problem is vital. Since the healthcare system has a limited capacity, it is essential to predict the pandemic's future trend to avoid overload. Our results predict that the US will have almost 16 million cases by the end of November. The upcoming peak in the number of infected, ICU admitted cases would be mid-to-end November. In the end, we proposed several managerial insights that will help the policymakers have a clearer vision about the growth of COVID-19 and avoid equipment shortages in healthcare systems. © 2021 Elsevier Ltd
dc.identifier.DOI-ID10.1016/j.eswa.2021.114920
dc.identifier.issn09574174
dc.identifier.urihttp://akademikarsiv.cbu.edu.tr:4000/handle/123456789/13197
dc.language.isoEnglish
dc.publisherElsevier Ltd
dc.rightsAll Open Access; Green Open Access
dc.subjectHealth care
dc.subjectStochastic systems
dc.subjectBenchmark functions
dc.subjectCOVID-19
dc.subjectGaussians
dc.subjectGradient based
dc.subjectGradient search
dc.subjectGray wolf optimizer
dc.subjectGray wolves
dc.subjectHealthcare systems
dc.subjectOptimizers
dc.subjectPandemic modeling
dc.subjectForecasting
dc.titleGradient-based grey wolf optimizer with Gaussian walk: Application in modelling and prediction of the COVID-19 pandemic
dc.typeArticle

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