An Optimal Controlling of COVID-19 Preventive Measures Variable for Environmental Sustainable Solution using Genetic Algorithm

This research study demonstrates how to utilize a Genetic Algorithm (GA) to manage variables as effectively as possible in order to prevent COVID-19. The new aspect of this system is the use of GA to train the variables for preventive measures and create a sustainable environment solution. A nation such as India had a steady rise in COVID-19 cases during the epidemic and a subsequent decline following the implementation of certain preventive measures. The covid-19 curve can be flattened and reduces when preventive actions are taken, however the main drawback of the previous approaches was that it showed poor mean square error, poor convergence rate, and increased sensitivity. Data collecting for COVID-19 cases in India during the first wave is where it starts. After that, a nonlinear multi-objective function is created using the variables related to preventive actions and trained using GA and linear regression analysis. Furthermore, if periodic preventive actions are implemented, it is anticipated that the COVID-19 curve will flatten and decrease, ensuring sustainability. Then, when compared to other current approaches, the COVID-19 curve exhibits a faster convergence rate, improved mean square error, and sensitivity when using GA instead.