Feature selection (FS) is a critical step in data mining, and machine learning algorithms play a crucial role in algorithms performance. It reduces the processing time and accuracy of the categories. In this paper, three different solutions are proposed to FS. In the first solution, the Harris Hawks Optimization (HHO) algorithm has been multiplied, and in the second solution, the Fruitfly Optimization Algorithm (FOA) has been multiplied, and in the third solution, these two solutions are hydride and are named MOHHOFOA. The results were tested with MOPSO, NSGA-II, BGWOPSOFS and B-MOABC algorithms for FS on 15 standard data sets with mean, best, worst, standard deviation (STD) criteria. The Wilcoxon statistical test was also used with a significance level of 5% and the Bonferroni–Holm method to control the family-wise error rate. The results are shown in the Pareto front charts, indicating that the proposed solutions' performance on the data set is promising.

特征选择（FS）是数据挖掘中的关键步骤，而机器学习算法在算法性能中起着至关重要的作用。它减少了处理时间和类别的准确性。本文针对FS提出了三种不同的解决方案。在第一个解决方案中，哈里斯霍克斯优化（HHO）算法已被乘以，在第二个解决方案中，Fruitfly优化算法（FOA）已被相乘，在第三种解决方案中，这两个解决方案都是氢化物，称为MOHHOFOA。使用MOPSO，NSGA-II，BGWOPSOFS和B-MOABC算法针对15个标准数据集（均值，最佳，最差，标准差（STD）标准）对FS进行了测试。还使用了显着性水平为5％的Wilcoxon统计检验和Bonferroni-Holm方法来控制家庭错误率。