Nature Inspired Computational Approach to Solve the Model for HIV Infection of CD4

In this paper, a stochastic heuristic technique is investigated to obtain the approximate solution of the HIV infection model of CD4T cells. The proposed technique represents the approximate solution as a linear combination of some polynomial basis functions with unknown adaptable coefficients. The trial solution of the problem is formulated using a fitness function, which contains unknown adaptable coefficients. The minimization of the fitness function is performed using the hybrid heuristic computational approach. The stochastic global search technique such as genetic algorithm (GA) is hybridized with two local search optimizers such as interior point algorithm (IPA) and active set algorithm (ASA), for obtaining the unknown coefficients. The effectiveness of the proposed technique is illustrated in contrast with fourth-order Runge Kutta method (RK4) and some well known deterministic standard methods. The results validate the accuracy and viability of the proposed technique for the approximate solution of the HIV infection model of CD4 T cells.