## 1 Introduction  
Machine learning and data mining are fast-growing topics in research and industry because of the massive amount of data being generated which needs to be converted into usable information. This conversion process plays an essential part in the process of knowledge discovery, as it comprises a set of repetitive task sequences including the transformation, reduction, cleansing, and integration of data, among others [1]. These steps are known as pre-processing; their outcome directly impacts the machine learning and data mining algorithm performance. Due to its importance, data is regarded as the \"currency\" of the present decade. This makes the correct handling of data a necessity. With the increase in data and the growth of machine learning and data mining, the processing of data is becoming more and more tedious. The increase in dimensionality means that training machine learning and data mining algorithms takes longer, making them more computationally expensive. Researchers have developed different methods to address the problem of dimensionality. One such method is feature selection, which removes the presence of noisy data such as unnecessary or useless features that do not assist with the purpose of classification [2]. Feature selection is a pre-processing stage which assists in selecting valuable features and separating them from a set of unwanted ones, thereby improving the performance of the classifiers. This method eradicates redundant, irrelevant features, thereby reducing time complexity [3]. Feature selection is generally performed in two ways, namely, wrapper and filter methods [4]. The wrapper method utilizes learning algorithm(s) to choose the subsets of features. This method produces better performance but is more computationally expensive than the filter method. The feature selection under the wrapper technique is referred to as an optimization problem [5]. The filter-based technique does not depend on a learning algorithm; rather, it chooses useful features by utilizing information gain, mutual information etc. [6]. This method is computationally inexpensive but does not produce as good a performance as the wrapper-based techniques. Finding the relevant subset of features is a challenging task, as the main aim is to select the minimum number of features and get the maximum accuracy possible. Due to the increased time required to locate the optimal subset of features, feature selection is referred to as an NP-hard problem [7]. Should we have an \(N\) feature, the sum of \(2^{N}-1\) of the number of the combination of features is needed to investigate and locate the best features [8]. The need for a high-performing metaheuristic to take care of this type of problem is important to reduce the processing time. The search processes of metaheuristics rely on the trade-off between the exploitation (intensification)--that conducts a thorough neighborhood search to obtain better possible solutions--and exploration (diversification)--that tests the solution of candidates not within the neighborhood. These two objectives are the factors that define the ability to find optimal solution(s). Recently, feature selection as an optimization problem has been solved using metaheuristic algorithms because they show better performance than exact methods [8; 9; 10; 11; 12; 13]. However, due to the no free lunch (NFL) theorem, which proposes that no one algorithm is sufficient to solve all optimization problems, the need to develop new or improve existing methods that can make high-quality solutions for the candidate problem becomes unavoidable. Despite the great effort and advancements in this area, most metaheuristic algorithms have at least one deficiency or shortcoming. Examples of such limitations include getting trapped in local optima, premature convergence, too many parameters to be tuned and so on.