# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
## 2 Related Works  
Metaheuristic algorithms are broadly employed to resolve optimization issues [23]. Metaheuristics are frequently used to determine the appropriate subset of features in a reasonable time. Such methods have been classified into a set of classes as classified in [24]. Such classes include physics-based [25; 26], social-based [27], music-based, chemistry-based, sport-based, math-based, biology-based [28], and swarm-based [20]. Since the focus of this work is on swarm-based metaheuristic optimizers, a more detailed listing of such metaheuristics is provided as follows. Examples of swarm-based metaheuristics include PSO [17; 18; 20; 29], Honey Bee Mating Optimization (HBMO) [30], Whale Optimization Algorithm (WOA) [31], Harris Hawks Optimizer (HHO) [32], Dragonfly Algorithm (DA) [33], Ant Colony Optimization (ACO) [34; 35], Ant Lion Optimizer (ALO) [36; 37; 38], GWO [15; 16; 21; 39; 40; 41], Bacterial Foraging Optimization (BFO) [42], Spotted Hyena Optimization (SHO) [43], and many others. Many researchers have proposed hybrid metaheuristics to improve the exploration and exploitation aspects of searching techniques to avoid local optima. For example, a hybrid firefly algorithm with a mutation operator was used for FS optimization to detect intrusions in a network [44]. This hybrid approach was utilized to choose the best features without becoming stuck in the local optima. A hybrid algorithm that combines WOA with the flower pollination algorithm for feature selection to detect a spam email was proposed in [45]. Genetical Swarm Optimization (GSO) was proposed in [46], which provided a combination of Genetic Algorithms (GAs) and PSO. This technique was used to optimize the FS problem for digital mammogram sets of data. Another hybrid approach was proposed in [47] that combined GWO and WOA to create a wrapper-based FS method. A hybrid model that combined SHO with Simulated Annealing (SA) for FS optimization is called SHOSA and was proposed in [43]. The findings indicated the superiority of SHOSA in finding the best features. A hybrid algorithm called (MBA-SA) combined the Mine Blast Algorithm (MBA) with SA to optimize the FS problem and is presented in [48]. A binary algorithm that combined GWO with HHO to select the optimal features from the datasets was proposed in [49]. This hybrid algorithm is called HBGWOHHO. The results indicated that the proposed algorithm outperformed some well-known FS optimization algorithms. A hybrid algorithm based on PSO to boost SHO's hunting methodology was proposed in [50]. The findings showed that the proposed algorithm outperformed four metaheuristic algorithms when tested on thirteen well-known benchmark functions including unimodal and multimodal functions. An improved binary GWO algorithm was proposed in [51]. This work attempted to improve the accuracy of the classification process when compared with the original BGWO algorithm. The proposed improved BGWO algorithm was analyzed by tuning a set of parameters in the original GWO algorithm to achieve an acceptable balance between the exploration and the exploitation capabilities of the original GWO. A set of approaches based on this sensitivity analysis were implemented and compared against each other. This work is similar to our work in the sense that it aimed to solve the GWO exploration-exploitation balance problem. However, our approach combines the binary PSO algorithm with the binary GWO algorithm instead of implementing a kind of sensitivity analysis like the one proposed in that work. An improved BPSO algorithm was proposed in [52]. This work aimed to enhance the exploration ability of the original PSO by modifying its population search process. This modification was based on applying two trained surrogate models, where each model was used to approximate the fitness values of the individuals in a separate sub-population. This work is also similar to our work in its ability to enhance the exploration capability ofthe original BPSO algorithm.