## 3 Original White Shark Optimizer (WSO)  
The WSO is a nature-inspired metaheuristic optimization algorithm developed by [PERSON] et al. in 2022 [13]. The fundamental principle of the algorithm is based on the dynamic and directional movements of white sharks during the hunting process. These creatures prefer to detect their prey using their sense of smell and hearing rather than directly chasing it. Their advanced hearing abilities enable them to explore the search space effectively. As illustrated in Figure 1, white sharks can detect changes in water pressure, electromagnetic waves, and wave frequencies through the lateral lines extending along both sides of their bodies, allowing them to locate the prey. The structure of the algorithm is designed to model exploration and exploitation behaviors in the solution space based on these biological features. WSO begins with a randomly generated initial population, where each individual is represented by a d-dimensional solution vector corresponding to the problem's dimensionality. The initial positions of individuals are randomly selected within predefined lower and upper bounds. The quality of each solution is evaluated using a fitness function. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Year** & **Algorithm** & **Instruction** \\ \hline
2014 & BBA [31] & A feature selection method based on the BAT algorithm and the Optimum-Path Forest classifier has been proposed. \\
2015 & DisABC [23] & The ABC algorithm has been discretized using bitwise operators, transfer functions, and a Jaccard similarity-based mutation strategy. \\
2016 & MFO [32] & The MFO algorithm, developed by drawing inspiration from the nocturnal flight strategy of moths, has been employed in the feature selection process. \\
2017 & WOASA [33] & To achieve optimal feature selection, a hybrid binary whale optimization algorithm (WOASA) was proposed by integrating Simulated Annealing (SA) with the WOA. A return-cost-based binary Firefly Algorithm (Rc-BBFA) has been proposed for feature selection. The method incorporates a mechanism specifically designed to prevent premature convergence. \\
2018 & MCSRS [35] & A modified cuckoo search-based RS (MCSRS) method with coarse clusters has been developed for feature selection. \\
2020 & TMGWO [36] & A novel GWO algorithm incorporating a two-stage mutation mechanism has been proposed for feature selection. \\
2020 & BMVO [24] & For feature selection, the MVO algorithm was discretized using transfer functions, and the BMVO algorithm was introduced. \\
2020 & BinSSA [28] & Spider Algorithm (BinSSA), was proposed. Additionally, transfer functions (S-Shape, V-Shape) and a crossover operator were utilized. \\
2021 & MPPSO [37] & A novel multi-population-based Particle Swarm Optimization (MPPSO) algorithm has been proposed for feature selection. \\
2021 & IDPSO-FS [25] & Using an entropy-based cut-point method, predefined cut-points were selected and optimized by PSO to discretize the features. \\
2022 & BAAA [11] & For feature selection, a binary version of the AAA was developed using transfer functions (S-Shape, V-Shape). \\
2023 & BIWSO [38] & A binary version of the WSO using transfer functions (S-shape, V-shape) has been developed for feature selection. \\
2023 & IBGJO [39] & An improved Binary Golden Jackal Optimization (IBGJO) was proposed for feature selection, using Chaotic Tent Map and Cosine Similarity to enhance solution quality and diversity. \\
2024 & HFPSO [40] & A hybrid metaheuristic algorithm combining PSO and FA methods is proposed for feature selection \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of swarm intelligence techniques employed in feature selection. The WSO algorithm operates based on three main behavioral models:  
* Moving towards prey: White sharks estimate the approximate position of their prey by sensing wave trails and odor signals. This orientation is modeled through velocity and position updates, allowing individuals to explore better regions in the solution space.