Understanding the early transmission dynamics of diseases and estimation of the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus (COVID-19). A mathematical model of severe SARS-CoV-2 transmission based on Wuhan's data is considered in this study. To solve the presented problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors such as contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing the contact rate and the transition rate of symptomatic infected individuals to the quarantined infected class, two optimal policies are designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying optimal policies, governments could find useful and practical ways for control of the disease.

中文翻译：

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控制新型冠状病毒病（COVID-19）爆发的最佳政策。


了解疾病的早期传播动态并评估控制政策的有效性对于流行病的预防具有必然的作用。为此，本文关注新型冠状病毒（COVID-19）的最优控制策略的设计。本研究考虑了基于武汉数据的严重 SARS-CoV-2 传播的数学模型。为了有效且高效地解决所提出的问题，提出了一种多目标遗传算法，以针对各种因素（例如有症状感染者的接触率和向隔离感染者的转移率）实现高质量的调度。通过改变有症状感染者的接触率和向隔离感染者的转移率，设计了两种最优策略。这项研究有两项主要科学贡献：(1) 这是提出有关 COVID-19 政策的开创性研究，(2) 这也是第一项针对 COVID-19 并通过多目标进化来考虑其经济后果的研究算法。数值模拟清楚地表明，通过应用最优政策，政府可以找到控制疾病的有用且实用的方法。