Features Selection (FS) approaches have more attention since they have been applied to several fields primarily to deal with high dimensional data. An increase in the dimension of data can lead to degradation of the accuracy of the machine learning method. Therefore, there are several FS methods based on meta-heuristic (MH) techniques that have been developed to tackle the FS problem and avoid the limitations of traditional FS approaches. However, those MH methods still need improvements that suffer from some drawbacks that affect the quality of the final output. So, this paper proposed a modified Henry Gas Solubility Optimization (HGSO) using enhanced Harris hawks optimization (HHO) based on Heavy-tailed distributions (HTDs). In this study, a dynamical exchange between five HTDs is used to boost the HHO that modifies, in turn, the exploitation phase in HGSO. As a result, we proposed a dynamic modified HGSO based on enhanced HHO (DHGHHD). To assess the efficiency of the proposed DHGHHD, a set of eighteen UCI datasets are used. Furthermore, it applied to improve the prediction of two real-world datasets in the drug design and discovery field. The DHGHHD is compared with eight well-known MH methods. Comparison results illustrate the high quality of DHGHHD according to the values of accuracy, fitness value, and the number of selected features.

由于特征选择（FS）方法已被应用到几个主要用于处理高维数据的领域，因此备受关注。数据维度的增加可能导致机器学习方法的准确性下降。因此，已经开发了几种基于元启发式（MH）技术的FS方法来解决FS问题并避免传统FS方法的局限性。但是，那些MH方法仍然需要进行改进，但会受到一些影响最终输出质量的缺点的困扰。因此，本文提出了一种改进的亨利气体溶解度优化（HGSO）方法，该方法利用了基于重尾分布（HTD）的增强型哈里斯鹰（Hawk hawks）优化（HHO）。在这项研究中，使用了五个HTD之间的动态交换来增强HHO，从而修改 HGSO的开发阶段。因此，我们提出了一种基于增强型HHO（DHGHHD）的动态修改HGSO。为了评估建议的DHGHHD的效率，使用了一组18个UCI数据集。此外，它还用于改善药物设计和发现领域中两个真实世界数据集的预测。DHGHHD与八种著名的MH方法进行了比较。根据准确性，适用性值和所选特征的数量，比较结果说明了DHGHHD的高质量。DHGHHD与八种著名的MH方法进行了比较。根据准确性，适用性值和所选特征的数量，比较结果说明了DHGHHD的高质量。DHGHHD与八种著名的MH方法进行了比较。根据准确性，适用性值和所选特征的数量，比较结果说明了DHGHHD的高质量。