关键词: 智能优化算法, 百度指数, 预警模型, 对比研究, 新冠疫情

Abstract:

Since the outbreak of Corona Virus Disease 2019（COVID-19） in Wuhan, China at the end of December 2019, my country’s economy and society have suffered great harm. Using network data to warn the development trend of the epidemic can effectively reduce its social harm. When using machine learning algorithms to build an early warning model, parameter selection is an important content, which is closely related to the accuracy of the final built model. It explores the application effects of a variety of new intelligent optimization algorithms in the Baidu search index COVID-19 early warning model, which can provide a certain theoretical basis and analysis strategy for the popularization and application of new intelligent optimization algorithms. Three new intelligent optimization algorithms, Multi-Verse Optimizer（MVO）, Slime Mould Algorithm（SMA） and Equilibrium Optimizer（EO）, are compared in the application effect of the Least Squares Support Vector Machine（LSSVM） in Baidu search index epidemic warning model. It optimizes algorithm optimization process, the SMA algorithm has poor convergence, and the global search ability is weaker than the MVO and EO algorithms, while the EO algorithm has relatively low computational efficiency. The MVO algorithm has high computational efficiency and strong convergence. Finally, an early warning model is constructed. The advantage is obvious（MSE=17.77, MAE=38.38, RMSE=129.3, R2=0.87）. The three intelligent optimization algorithms can all improve the prediction performance of the LSSVM early warning model, but the MVO optimization algorithm has the best comprehensive computing performance. The final MVO-LSSVM early warning model can be used to predict epidemic prevention behaviors in the subsequent epidemic prevention and control stage certain reference.

Key words: intelligent optimization algorithm, Baidu index, early warning model, comparative study, COVID-19