In the investigations presented here, an efficient computing approach is applied to solve Human Immunodeficiency Virus (HIV) infection spread. This approach involves CD4+ T-cells by feed-forward artificial neural networks (FF-ANNs) trained with particle swarm optimization (PSO) and interior point method (IPM), i.e., FF-ANN-PSO-IPM. In the proposed solver FF-ANN-PSO-IPM, the FF-ANN models of differential equations are used to develop the fitness functions for an infection model of T-cells. The training of networks through minimization problem are proficiently conducted by integrated heuristic capability of PSO-IPM. The reliability, stability and exactness of the proposed FF-ANN-PSO-IPM are established through comparison with outcomes of standard numerical procedure with Adams method for both single and multiple autonomous trials with precision of order 4 to 8 decimal places of accuracy. The statistical measures are effectively used to validate the outcomes of the proposed FF-ANN-PSO-IPM.

在这里介绍的调查中，一种有效的计算方法被应用于解决人类免疫缺陷病毒（HIV）感染的蔓延。该方法通过前馈人工神经网络（FF-ANN）训练CD4 + T细胞，该人工神经网络经过粒子群优化（PSO）和内点法（IPM）训练，即FF-ANN-PSO-IPM。在提出的求解器FF-ANN-PSO-IPM中，使用微分方程的FF-ANN模型开发T细胞感染模型的适应度函数。通过最小化问题对网络进行训练，可以通过PSO-IPM的综合启发式能力来进行。可靠性 FF-ANN-PSO-IPM的稳定性和准确性是通过与Adams方法对单次或多次自主试验的标准数值程序的结果进行比较而建立的，精度为4到8个小数位。统计方法可有效地用于验证拟议的FF-ANN-PSO-IPM的结果。