Four nature-inspired optimizers are combined with a multilayer perceptron neural network for reaching an optimal structure aimed at predicting the overall heat transfer coefficient of a ribbed triple-tube heat exchanger in terms of the rib pitch, rib height, and nanoparticle concentration. The heat exchanger works with a hybrid nanofluid having graphene nanoplatelet/Pt nanocomposite powder. The applied algorithms incorporate Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Ant Lion Optimizer (ALO), and Biogeography-Based Optimization (BBO). The required data are provided via computational solutions. The BBO algorithm is determined as the best method to predict the output because of its higher accuracy. The highest performance of the BBO is obtained from population of 450. The overall heat transfer coefficient is estimated with root mean square errors of 0.030 and 0.025 for the training data and testing data, respectively. Furthermore, the ACO algorithm shows the lowest computational time among all the methods.

四个受自然启发的优化器与多层感知器神经网络相结合，以达到一种最佳结构，该结构旨在根据肋节距，肋高和纳米颗粒浓度预测肋三重管式换热器的整体传热系数。热交换器与具有石墨烯纳米片/ Pt纳米复合材料粉末的混合纳米流体一起工作。应用的算法包括人工蜂群（ABC），蚁群优化（ACO），蚁狮优化器（ALO）和基于生物地理的优化（BBO）。所需数据通过计算解决方案提供。由于BBO算法具有较高的准确性，因此被确定为预测输出的最佳方法。BBO的最高性能来自450人口。对于训练数据和测试数据，估计总传热系数的均方根误差分别为0.030和0.025。此外，在所有方法中，ACO算法的计算时间最短。