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

Abstract

This 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.

The evolution mechanism of the multi-value chain network ecosystem supported by the third-party platform

“…Since Michael E. Porter put forward the value chain theory, the optimization and operation of the value chain have been widely concerned by scholars. They mainly discuss evolution trend forecasting, site selection, logistics transportation optimization, purchasing and inventory optimization, product portfolio optimization and sustainable development in the value chain [7,8,15,16,23,41,9,17,11,26,13,14,10,32,27,28]. For example, by using artificial intelligence tools, Goli et al [7] established a multi-stage model to study the evolution trend of demand for dairy products and found that demographic behavior and inflation rate are the main factors affecting dairy consumption.…”

“…[15]; Khalilpourazari et al[16]; Lotfi et al[23]; Tirkolaee et al[41];Goli et al [9][17]; Goli et al[11]; Lotfi et al[26]; Khalilpourazari et al[13]; Khalilpourazari et al[14]; Goli et al[10]; Pahlevan et al[32]; Lotfi et al[27]; Lotfi et al[28] …”

Investigation of robustness of hybrid artificial neural network with artificial bee colony and firefly algorithm in predicting COVID-19 new cases: case study of Iran

“…The findings disclosed that the proposed model can render a proper approximation of COVID-19 trend. By using gradient-based grey wolf optimizer, Khalilpourazari et al ( 2021 ) have published a study concerning COVID-19 prediction. First, they compared their algorithm with some benchmark algorithms and concluded that the proposed model is more accurate.…”

Kısıtlı Mühendislik Problemlerinin Karşılaştırmalı Ağırlık ve Maliyet Optimizasyonu

“…Her problem tipine göre en başarılı algoritmanın değişmesi, yeni meta-sezgisel algoritmaların oluşturulmasına ya da mevcutların geliştirilmesine imkan tanır. Literatürde optimizasyon algoritmalarının geliştirilmiş (iyileştirilmiş) versiyonları ile ilgili [2][3][4][5], algoritmaların birleştirilmesi ile oluşturulan hibrit algoritmalar [4][5][6][7][8][9] ile ilgili ya da yeni oluşturulan algoritmalarla ilgili çalışmalara rastlamak mümkündür.…”