## 2 Related Work  
A detailed review of related studies on the subject of the concept described in this study is presented in this section. The literature shows that several binary metaheuristic algorithms have been developed to solve the feature selection problem. The feature selection technique based on the wrapper approach uses the binary search capability of metaheuristic algorithms. Swarm- and evolutionary-based algorithms are becoming commonplace methods in the feature selection domain [15]. Particle Swarm Optimization (PSO) [16] is a bio-inspired metaheuristic method which has attracted much attention due to its tested and trusted mathematical modelling. This algorithm has been binarized and enhanced to solve problems in discrete search spaces. A study by [PERSON] and [PERSON] [17], presented a modified discrete PSO that used the logistic regression model and applied it to the feature selection domain. A year later, [PERSON] et al. [18], proposed an improved BPSO that introduced the effect of catfish, called \"catfishBPSO\", for feature selection. The BPSO was also improved to tackle the optimization problem of feature selection [19]. [PERSON] et al. [13], proposed an improved PSO based on the Levy flight local factor, a weighting inertia coefficient based on the global factor and a factor of improvement based on the mechanism of mutation diversity, called (IPSO), to tackle the feature selection problem. This improvement came with shortcomings, however, such as the inclusion of more parameters compared with other improved versions of the PSO, which makes tuning difficult for various application problems and increases computational time. Since every particle in BPSO moves closer to and farther from the hypercube corner, its major shortcoming is stagnation. The genetic algorithm (GA) is another popular bio-inspired feature selection method which has been widely utilized as a wrapper-based technique. [PERSON] and [PERSON] [20], proposed a GA-based method using the support vector machine (SVM) as a learning algorithm to solve the feature selection problem. The major goal of their work was concurrent parameter and feature subset optimization without reducing the classification accuracy of the SVM. The method reduced the number of feature subsets and improved the accuracy of classification but was outperformed by the Grid algorithm. Later, [PERSON] et al. [21], presented a hybrid GA and ant colony optimizer (ACO) as a feature selection method to predict protein functions. These two algorithms were combined to enable better and faster capabilities with very low computational complexity. Furthermore, [PERSON] et al. [22], proposed a modified GA (MGA), i.e., a feature selection method using a pre-trained deep neural network (DNN) for the prediction of the demand for different patients' key resources in an outpatient department. Apart from these two notable algorithms, several other nature-inspired methods have been utilized to solve feature selection problems. The binary wrapper-based bat algorithm was developed by [PERSON] et al. in 2012 [PERSON] et al. (2012). It uses the classifier optimum-path forest to locate the feature sets that produce maximum classification accuracy. [PERSON] et al. (2014), proposed a binary artificial bee colony (ABC) that employed a similarity search mechanism inspired by evolution to resolve the feature selection problem. [PERSON] et al. (2011), proposed the binary ant lion optimizer (BALO) which utilizes the transfer function as a means of moving ant lions within a discrete search space. The binary grey wolf optimizer with two techniques was proposed the following year to locate a subset of features that cater for the two conflicting objectives of the feature selection problem, i.e., to maximize the accuracy of the classification and minimize the number of selected features. However, this method was plagued with premature convergence, despite its outperformance of other methods used for comparison in the study. [PERSON] et al.