## 3 The Proposed Model
### Algorithmic Procedure of RHSO-FS Technique  
To select an optimal set of features, the RHSO-FS method is used. The RHSO technique is a metaheuristic based on the natural behaviors of a rock hyrax swarm [24]. The proposed technique simulates the group behavior of rock hyrax swarms in search of food and their unique method of searching for it. The rock hyrax lives in groups or colonies, with the dominant male closely following the colony to ensure its safety. The proposed model finds a better solution by integrating prior knowledge and local heuristics to construct a better subset of features for optimizing the performance of the classification. The overall working process RHSOFS system splits the total data into testing and training sets. The training dataset was entered into the optimization method \((f(x))\) for finding the optimal feature. The classification technique is given the optimal set of features \((f(x))\), trained, and given the test dataset for evaluating the performance of the model. Equation (1) is utilized for representing the selection of optimum features. Equation (2) decreases the error in all the iterations using the specified features, which increases the performance of the classification. The regulatory parameters for population-based algorithms are the initial weighting factors, population size, generation number, probabilities of crossover and mutation, and social and cognitive scaling factors. To accomplish good performance, these parameters needs to be finetuned, and the accuracy of the optimization method can be defined by the fine-tuning parameters; otherwise, the parameter values might result in an optimization technique leading to local optima, which would increase the computation cost of the optimization technique. The RHOSFS method is used for overcoming the aforementioned concern. This model is used for creating the optimum subset of input features for increasing the classification performance. The presented RHOSFS approach is briefly discussed in the following:  
First, select, examine, and generate a random sample of a \((0,1)\) binary population for the overall amount of input attributes for \(FS\). Generate a feature subset which is equivalent to 1 for all the representations of the input population. For computing the fitness values, the derived optimum input feature is given to the classification algorithm. The study aims at finding an optimal subset of input features which minimize the fitness model while enhancing the performance. \[err\ (x_{i})=actual\_output\ (x_{i})-model\_estimated\_output(x_{i}) \tag{1}\]  
\[fitness\ (x)=\frac{\sum_{x=0}^{n}\ err(x)}{n} \tag{2}\]  
\[leader=r_{1}\times x(leader_{pos},j) \tag{3}\]  
From the expression, \(r_{1}\) is a randomly generated number ranging from zero to one, \(x\) specifies the preceding location of the leader, \(leader_{pos}\) indicates the older location ofthe leader, and \(j\) represents the \"each diminution\". After updating the position of the leader, every member (or searching agent) updates its locations according to the following equation. \[x(i,j)=(x(i,j)-(circ\times x(i,j)+leader)) \tag{4}\]  
In Equation (4), \(circ\) indicates the circular movement, which attempts to replicate the circle system as follows:  
\[circ=sqrt\left(n_{1}^{2}+n_{2}^{2}\right) \tag{5}\]  
\[n_{1}=r_{2}\times cos(ang) \tag{6}\]  
\[n_{2}=r_{2}\times sin(ang) \tag{7}\]  
where \(r_{2}\) indicates the radius and denotes the random integer lies within \([0,\,1]\), and \(ang\) represents the angle of movement and is an arbitrary integer within \([0,\,360]\) in Equations (6) and (7). In each generation, the \(ang\) is also upgraded, and it depends on \(lb\) and \(ub\), the lower and upper limits of the parameters, correspondingly. \[dalta=random[lb,\,ub] \tag{8}\]  
\[ang=ang+dalta \tag{9}\]  
When the output values become greater than 360, or lesser than 0, then the angle \((ang)\) is fixed as 360 or 0 to hold it within a desirable range. Algorithm 1 describes the pseudocode of the RHSOFS.