The aim of this paper is twofold. First, black hole algorithm (BHA) is proposed as a new training algorithm for feedforward neural networks (FNNs), since most traditional and metaheuristic algorithms for training FNNs suffer from the problem of slow coverage and getting stuck at local optima. BHA provides a reliable alternative to address these drawbacks. Second, complementary learning components and Levy flight random walk are introduced into BHA to result in a novel optimization algorithm (BHACRW) for the purpose of improving the FNNs’ accuracy by finding optimal weights and biases. Four benchmark functions are first used to evaluate BHACRW’s performance in numerical optimization problems. Later, the classification performance of the suggested models, using BHA and BHACRW for training FNN, is evaluated against seven various benchmark datasets: iris, wine, blood, liver disorders, seeds, Statlog (Heart), balance scale. Experimental result demonstrates that the BHACRW performs better in terms of mean square error (MSE) and accuracy of training FNN, compared to standard BHA and eight well-known metaheuristic algorithms: whale optimization algorithm (WOA), biogeography-based optimizer (BBO), gravitational search algorithm (GSA), genetic algorithm (GA), cuckoo search (CS), multiverse optimizer (MVO), symbiotic organisms search (SOS), and particle swarm optimization (PSO). Moreover, we examined the classification performance of the suggested approach on the angiotensin-converting enzyme 2 (ACE2) gene expression as a coronavirus receptor, which has been overexpressed in human rhinovirus-infected nasal tissue. Results demonstrate that BHACRW-FNN achieves the highest accuracy on the dataset compared to other classifiers.

本文的目的是双重的。首先，黑洞算法 (BHA) 被提出作为前馈神经网络 (FNN) 的一种新训练算法，因为大多数用于训练 FNN 的传统和元启发式算法都存在覆盖速度慢和陷入局部最优的问题。BHA 提供了一种可靠的替代方案来解决这些缺点。其次，将互补学习组件和 Levy 飞行随机游走引入 BHA，从而产生一种新颖的优化算法 (BHACRW)，目的是通过找到最佳权重和偏差来提高 FNN 的准确性。首先使用四个基准函数来评估 BHACRW 在数值优化问题中的性能。随后，使用 BHA 和 BHACRW 训练 FNN 的建议模型的分类性能针对七个不同的基准数据集进行评估：虹膜、葡萄酒、血液、肝脏疾病、种子、Statlog（心脏）、天平。实验结果表明，与标准 BHA 和八种著名的元启发式算法相比，BHACRW 在均方误差 (MSE) 和训练 FNN 的准确性方面表现更好：鲸鱼优化算法 (WOA)、基于生物地理学的优化器 (BBO)、引力搜索算法 (GSA)、遗传算法 (GA)、布谷鸟搜索 (CS)、多元宇宙优化器 (MVO)、共生生物搜索 (SOS) 和粒子群优化 (PSO)。此外，我们检查了建议的方法对血管紧张素转换酶 2 (ACE2) 基因表达作为冠状病毒受体的分类性能，该受体已在人类鼻病毒感染的鼻组织中过度表达。