A novel design of evolutionally computing to study the quarantine effects on transmission model of Ebola virus disease

In recent years, hybrid genetic algorithms have attracted a lot of attention and are being utilized more frequently to address real-world issues. A genetic algorithm can combine multiple approaches within its framework to create a hybrid that benefits the most from the combination. In this article, a new methodology of hybrid genetic algorithm using sequential quadratic programming (HGASQP) is presented to optimize the transmission dynamics of the Ebola virus disease model (EVDM) by implementing the intelligent paradigm of feed-forward neural networks. The Ebola virus, also known as Ebola haemorrhagic, is a transmitter virus that was initially transferred to humans by wild and domestic animals, and then it spread from human to human. To control this spread, a mathematical model is proposed consist of susceptible-S, exposed-E, infected-I, quarantined-Q and recovered-R classes. Coupling GA with local search SQP take the advantages of both for determining the global optimum as well as local optimum while at the same time also providing fast convergence. The mean squared error (MSE) is minimized as fitness objective function to optimize HGASQP. The optimized solutions of HGASQP are compared with the numerical results of the Adam approach in order to validate the effectiveness and robustness of the proposed algorithm. Furthermore, statistical and quantitative analysis are also established to authenticate the performance of HGASQP. It has been demonstrated that the suggested optimization approach is precise and can boost optimization effectiveness.