One of the main challenges in the field of deep learning is obtaining the optimal model hyperparameters. The search for optimal hyperparameters usually hinders the progress of solutions to real-world problems such as healthcare. Previous solutions have been proposed, but they can still get stuck in local optima. To overcome this hurdle, we propose OptBA to automatically fine-tune the hyperparameters of deep learning models by leveraging the Bees Algorithm, which is a recent promising swarm intelligence algorithm. In this paper, the optimization problem of OptBA is to maximize the accuracy in classifying ailments using medical text, where initial hyperparameters are iteratively adjusted by specific criteria. Experimental results demonstrate a noteworthy enhancement in accuracy with approximately 1.4%. This outcome highlights the effectiveness of the proposed mechanism in addressing the critical issue of hyperparameter optimization and its potential impact on advancing solutions for healthcare. The code is available publicly at \url{https://github.com/Mai-CS/OptBA}.

翻译：深度学习领域的主要挑战之一在于获取最优模型超参数。超参数寻优过程通常会阻碍医疗健康等现实问题解决方案的进展。现有解决方案虽已提出，但仍可能陷入局部最优。为克服此障碍，我们提出OptBA方法，通过利用近期备受关注的群体智能算法——蜂群算法，实现深度学习模型超参数的自动微调。本文中，OptBA的优化目标在于最大化医学文本疾病分类的准确率，其通过特定准则对初始超参数进行迭代调整。实验结果表明准确率获得约1.4%的显著提升，这凸显了所提机制在解决超参数优化关键问题上的有效性及其对推动医疗健康解决方案发展的潜在影响。代码已公开于 \url{https://github.com/Mai-CS/OptBA}。