## 3 Proposed Methodology
### OWSA (Optimal Whale Sine Algorithm) for Feature Selection
The spiral positioning of the whale is given in Equation (3),  
\[\overset{\rightarrow}{z}(t+1)=\overset{\rightarrow}{r}^{\prime}\left(e^{bl }\right)(cos(2\pi l))+\overset{\rightarrow}{z}^{*}(t) \tag{4}\]  
In Equation (4), \(\overset{\rightarrow}{r}^{\prime}=\left|\overset{\rightarrow}{z}^{*}(t)- \overset{\rightarrow}{z}(t)\right|\) reveals the distance of the \(i\)th whale from the prey (satisfactory outcome gained here), and \(b\) represents the constant defining the logarithmic spiral format. \(\overset{\rightarrow}{z}(t+1)\) is the coefficient vector. \(\overset{\rightarrow}{r}^{\prime}\left(e^{bl}\right)(cos(2\pi l))+\overset{ \rightarrow}{z}^{*}(t)\) denotes the coefficient scalar. \(l\) is a random digit (in (\(-1\), \(1\))). The stepwise procedure for WOA is given in Pseudocode 1. ``` \begin{tabular}{l} \hline \hline
**Pseudocode 1:** WOA (Whale Optimization Algorithm) \\ \hline Modify the whale populace \(z_{i}\) (\(i=1,2,\ldots,n\)) \\ Analyze the value of fitness function \\ Randomly select the search agent \(z^{*}\) \\
**Initialize**\(t=1\) and \(t<\)_maximum iterations_ \\
**for** every search agent \\ Modify \(a,\ p,\ q,\ l\), and \(x//a\) represents linearly reduced within the range of 2 to 0 over the \\ course of iteration, \(p\) and \(q\) represents coefficient vectors, \(l\) represents absolute value \\ and \(x\) represents the constant for explaining the shape of the logarithmic spiral. \\
**if**\(1\) (\(p<0.5\)) \\
**if**\(2\) (\(|a|<1\)) \\ Update the position of current solution by Equation (2) \\
**else if**\(2\) (\(|a|\geq 1\)) \\ Update the position of current solution by Equation (3) \\
**end if**\(2\) \\
**else if**\(1\) (\(p\geq 0.5\)) \\ Random solution is generated \\ Update the position of the current solution by Equation (4) \\
**endif if**\(1\) \\
**end for** \\ if some search agent drives away from the search space or else amend it \\ Analyze the value of fitness function \\ Modify \(z^{*}\) if it is better \\ \(t=t+1\) \\
**end while** \\ return \(z^{*}\) \\ \hline \hline \end{tabular} 

Based on the pseudocode, the whale population is altered and the fitness values are assessed. Thus, the search agent is modified and the ideal search agent is finally attained. On the contrary, the SCA (Sine Cosine Algorithm) is considered; the trigonometric operations of SC are the basis for this algorithm. Typically, SCA shows a better acceleration and convergence rate. It also shows a reliable implementation time. Several initial random solutions are generated by the SCA. It also helps in transferring the ideal solution that employs a mathematical framework in accordance with the functionalities of SC. Varied random and adaptive variables are integrated within this algorithm to maintain the search spaces in the optimization processes. The population search approach and local search approach are the major techniques in SCA. This algorithm shows certain innate advantages, such as simple execution and flexibility, due to which it could be utilized for solving several optimization issues. These features have enabled SCA to resolve various optimization issues. Taking into account the n-dimension optimization issue,

\[\underset{f}{min}(a_{1},\ a_{2}\ldots a_{n}) \tag{5}\]

\[l_{i}\leq a_{i}\leq u_{i};\ i=1,\ 2\ldots n \tag{6}\]

In Equation (5), \(a_{i}\) indicates the ith-decision variable, \(l_{i}\) denotes the lower bound, and \(u_{i}\) represents the upper bound. Moreover, \(n\) indicates the problem dimension. In Equation (5), SCA utilizes the oscillatory functions of both functions of SC, which alters the capacities of individuals to observe the global ideal solution. The precise process is given below. An assumption is made such that, in the SCA, the population size is termed \(N\), and the \(i\)th individual's position in the \(t\)th generation is represented as \((a_{i}^{\dagger}=a_{i1}^{\dagger}+a_{i2}^{\dagger},\ldots,a_{in}^{\dagger})\), wherein \(i=1,2,\ldots N\).