## 1 Introduction  
Data mining is determined as an important step in the knowledge discovery process. It has become an active research domain due to the presence of huge collections of digital data that need to be explored and transformed into useful patterns. The main role of data mining is to develop methods that assist in finding potentially useful hidden patterns in huge data collections [1]. In data mining techniques such as classification, preprocessing of data has a great influence on the goodness of discovered patterns and the efficiency of machine learning classifiers [1, 2]. Feature selection (FS) is one of the main preprocessing techniques to discover and retain informative features and eliminate noisy and irrelevant ones. Selecting the optimal or near-optimal subset of given features will enhance the performance of the classification models and reduce the computational cost [2, 3, 4]. Based on the evaluation criteria of the selected features subset, FS approaches are classified into two classes: filter and wrapper approaches [3]. Filter techniques depend on scoring matrices such as chi-square and information gain to estimate the quality of the picked subset of features. More accurately, in filter approaches, a filter approach (e.g., chi-square) is used to rank the features, and then the only ones that have weights greater than or equal to a predefined threshold are retained. In contrast, wrapper approaches mainly consider a machine learning classifier such as K-Nearest Neighbors (KNN) or Support Vector Machines (SVM) to evaluate the feature subset. Another aspect for categorizing FS methods is based on the selection mechanism that is used to explore the feature space, searching for the most informative features. The search algorithm task is to generate subsets of features, and then the machine learning algorithm is applied to assess the generated subsets of features to find the optimal one [4, 5, 6]. Compared to filter approaches, wrappers have superior performance, especially in terms of accuracy since it considers the dependencies between features in the dataset, while filter FS may ignore such relations [7]. Although, filter FS is better than wrapper FS in terms of computational cost [4]. Commonly, for a wide range of data mining applications, reaching the optimal subset of features is a challenging task. The size of the search space grows exponentially with respect to the number of features (i.e., \(2^{K}-1\) possible subsets can be generated for a dataset with \(k\) features). Accordingly, FS is an intractable NP-hard optimization problem in which exhaustive search and even conventional exact optimization methods are impractical. For that reason, the FS domain has been extensively investigated by many researchers [5, 8]. For example, in [9], an improved version of the binary Particle Swarm Optimization (PSO) algorithm was introduced for the FS problem. An unsupervised FS approach based on Ant Colony Optimization (ACO) was proposed by [10]. Moreover, an FS technique that hybrids Genetic Algorithm (GA) and PSO was introduced in [11]. Finally, a binary variant of the hybrid Grey Wolf Optimization (GWO) and PSO is presented in [12] to tackle the FS problem. Meta-heuristic algorithms have been very successful in tackling many optimization problems such as data mining, machine learning, engineering design, production tasks, and FS [13]. Meta-heuristic algorithms are general-purpose stochastic methods that can find a near-optimal solution within a reasonable time. Lately, various Swarm Intelligence (SI) based meta-heuristics have been developed and proved a good performance for handling FS tasks in different fields [14, 15]. Some examples include While Optimization Algorithm (WOA) [16], Slim Mould Algorithm (SMA) [17], Marine Predators Algorithm (MPA) [18], and Grey Wolf Optimizer (GWO) [19]. Recently, [PERSON] and his co-authors proposed a new nature-inspired meta-heuristic optimizer named Harris Hawks Optimization (HHO) [20].