Swarm Intelligent Computing Procedures to Solve the Novel Precautionary Measures in the Nonlinear HIV System

Abstract

The current investigations present a novel prevention dynamic in the Human immunodeficiency virus (HIV) system, which is one of the deathly, convertible and unsafe viruses that arise almost the entire world. Since there is currently no confirmed remedy or medication, therefore the researchers are motivated to offer a mathematical preventive group in HIV, which makes this system HIPV. This novel category includes the injectable medicine, avoid getting pregnant, safety precautions, and contact frequency. The numerical solutions of the HIPV model are presented by applying the artificial neural networks (ANNs), while the hybridization of global particle swarm optimization (PSO) and local interior-point algorithm (IPA) approaches are used in the optimization process. The correctness of the solver ANNs-PSO-IPA is observed by using the comparison of the results, which are based on proposed and reference Runge–Kutta scheme as well as minor absolute error, while some statistical measures are performed in order to get the reliability of the designed solver. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.