COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process

Abstract

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.