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.

中文翻译：

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COVID-19 优化器算法、冠状病毒分布过程的建模和控制。

新型冠状病毒肺炎 (COVID-19) 的出现导致公共卫生部门不堪重负，死亡率很高。当务之急是控制疫情、降低感染率。当务之急是确保全民保持严格的社交距离，从而减缓疫情的传播。因此，除了应用优化问题外，还需要一种能够解决 NP 难题的高效优化器算法。本文首先提出了一种新颖的 COVID-19 优化器算法（CVA）来覆盖优化问题的几乎所有可行区域。我们还模拟了全球多个国家的冠状病毒传播过程。然后，我们将冠状病毒的分布过程建模为优化问题，以最大程度地减少受 COVID-19 感染的国家数量，从而减缓流行病的传播。此外，我们提出了三种场景来使用分配过程中最有效的因素来解决优化问题。仿真结果表明，其中一种控制方案优于其他方案。使用多种优化方案的广泛模拟表明，CVA 技术表现最佳，与火山喷发算法 (VEA)、灰狼优化器 (GWO)、粒子群优化 (PSO) 相比，性能提升高达 15%、37%、53% 和 59%和遗传算法（GA）。