论文标题
使用强大的多目标优化和随机分形搜索来鉴定流行病学模型，以模拟COVID-19的流行
Identification of an Epidemiological Model to Simulate the COVID-19 Epidemic using Robust Multi-objective Optimization and Stochastic Fractal Search
论文作者
论文摘要
传统上，在逆问题的制定和解决方案中识别参数认为模型，变量和数学参数没有不确定性的考虑。这方面简化了估计过程，但没有考虑到目标函数方面的设计变量相对较小的变化的影响。在这项工作中，SIDR（易感，感染，死亡和恢复）模型用于模拟新型冠状病毒疾病的动态行为（Covid-19），并且通过提出强大的反面问题来估算其参数，即考虑设计变量的敏感性。为此，考虑到与估计过程相关的不确定性以及鲁棒性参数最大化的不确定性的最小化，就提出了强大的多目标优化问题。为了解决这个问题，多目标随机分形搜索算法与评估鲁棒性的有效平均概念有关。考虑到中国流行病的真实数据获得的结果表明，对设计变量的敏感性的评估可以提供更可靠的结果。
Traditionally, the identification of parameters in the formulation and solution of inverse problems considers that models, variables and mathematical parameters are free of uncertainties. This aspect simplifies the estimation process, but does not consider the influence of relatively small changes in the design variables in terms of the objective function. In this work, the SIDR (Susceptible, Infected, Dead and Recovered) model is used to simulate the dynamic behavior of the novel coronavirus disease (COVID-19), and its parameters are estimated by formulating a robust inverse problem, that is, considering the sensitivity of design variables. For this purpose, a robust multi-objective optimization problem is formulated, considering the minimization of uncertainties associated to the estimation process and the maximization of the robustness parameter. To solve this problem, the Multi-objective Stochastic Fractal Search algorithm is associated with the Effective Mean concept for the evaluation of robustness. The results obtained considering real data of the epidemic in China demonstrate that the evaluation of the sensitivity of the design variables can provide more reliable results.
