The aim of this study is to analysis the mass and heat transfer in radiative three dimensional flow of hybrid nanofluid over the stretchable sheet by exploiting the strength of integrated computational intelligent algorithm by utilization of Gaussian wavelet neural networks (GWNNs) trained with the genetic algorithms (GAs) based global search supported with sequential quadratic programming (SQP) based local refinements i.e., GWNN-GA-SQP. The mean squared error based cost function is developed for the fluidic problem by applying Gaussian WaveNet GWNNs optimize with GAs and SQP. The numerical outcomes of the fluidic model are obtained by the proposed GWNN-GA-SQP solver to examine the thermal and velocities profile effect for three physical quantities based on magnetic parameter, nanomaterial concentration and transformated angular velocity. Moreover, a exhaustive analysis of the numerical solutions of GWNN-GA-SQP solver with reference Adams method endorse the stability, accuracy and consistency on multiple autonomous runs through different statistical performance operators and complexity analysis.

这项研究的目的是通过利用遗传算法训练的高斯小波神经网络（GWNN）来利用集成计算智能算法的优势，以分析可拉伸片材上混合纳米流体在辐射三维流中的质量和热传递。基于GA的全局搜索，并支持基于顺序二次规划（SQP）的局部优化，即GWNN-GA-SQP。通过应用经GA和SQP优化的高斯WaveNet GWNN，针对流体问题开发了基于均方误差的成本函数。所提出的GWNN-GA-SQP求解器获得了流体模型的数值结果，以基于磁参数，纳米材料浓度和变换的角速度来检查三个物理量的热和速度剖面效应。而且，