The significant growth of modern technology and smart systems has left a massive production of big data. Not only are the dimensional problems that face the big data, but there are also other emerging problems such as redundancy, irrelevance, or noise of the features. Therefore, feature selection (FS) has become an urgent need to search for the optimal subset of features. This paper presents a hybrid version of the Harris Hawks Optimization algorithm based on Bitwise operations and Simulated Annealing (HHOBSA) to solve the FS problem for classification purposes using wrapper methods. Two bitwise operations (AND bitwise operation and OR bitwise operation) can randomly transfer the most informative features from the best solution to the others in the populations to raise their qualities. The Simulate Annealing (SA) boosts the performance of the HHOBSA algorithm and helps to flee from the local optima. A standard wrapper method K-nearest neighbors with Euclidean distance metric works as an evaluator for the new solutions. A comparison between HHOBSA and other state-of-the-art algorithms is presented based on 24 standard datasets and 19 artificial datasets and their dimension sizes can reach up to thousands. The artificial datasets help to study the effects of different dimensions of data, noise ratios, and the size of samples on the FS process. We employ several performance measures, including classification accuracy, fitness values, size of selected features, and computational time. We conduct two statistical significance tests of HHOBSA like paired-samples T and Wilcoxon signed ranks. The proposed algorithm presented superior results compared to other algorithms.

中文翻译：

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用于特征选择的模拟退火混合 Harris Hawks 优化算法

现代技术和智能系统的显着增长已经产生了大量的大数据。不仅是大数据面临的维度问题，还有其他新出现的问题，如特征的冗余、不相关或噪声。因此，特征选择（FS）成为寻找最优特征子集的迫切需要。本文提出了一种基于位运算和模拟退火 (HHOBSA) 的 Harris Hawks 优化算法的混合版本，以使用包装器方法解决 FS 问题以进行分类。两个按位运算（AND 按位运算和 OR 按位运算）可以将信息量最大的特征从最佳解决方案随机转移到种群中的其他解决方案，以提高它们的质量。模拟退火 (SA) 提高了 HHOBSA 算法的性能并有助于摆脱局部最优。具有欧几里得距离度量的标准包装方法 K 最近邻用作新解决方案的评估器。基于 24 个标准数据集和 19 个人工数据集，呈现了 HHOBSA 与其他最先进算法之间的比较，它们的维度大小可达数千。人工数据集有助于研究不同维度的数据、噪声比和样本大小对 FS 过程的影响。我们采用了多种性能指标，包括分类准确度、适应度值、所选特征的大小和计算时间。我们对 HHOBSA 进行了两个统计显着性检验，如配对样本 T 和 Wilcoxon 符号秩。