## 3 Proposed Methodology
### OWSA (Optimal Whale Sine Algorithm) for Feature Selection
In addition, the fitness value is computed for individuals, and the position of the ideal individual is recorded as \(a_{*}^{t}\) (\(a_{*}^{t}=arg-min\Big{(}f_{a_{i}^{t}}\Big{)}\). The \(j\)th dimension for the \(i\)th individual in the populace is updated in Equation (7),

\[ \begin{array}{c}a_{ij}^{t}(t+1)=\Big{\{}a_{ij}^{t}+d_{1}(sin(r_{2})).\Big{|}d _{3}\Big{(}a_{*j}^{t}-a_{ij}^{t}\Big{|};\,d_{4}<0.5\\ a_{ij}^{t}+d_{1}(cos(r_{2})).\Big{|}d_{3}\Big{(}a_{*j}^{t}-a_{ij}^{t}\Big{|};\,d_ {4}\geq 0.5\end{array}  \tag{7}\]

In Equations (6) and (7), \(d_{3}\in(0,\,2\pi)\) and \(d_{4}\in(0,\,1)\) indicate the random count of the uniform distribution, wherein \(d_{1}\) indicates the control parameter. The computation process is given in Equation (8),

\[d_{1}=x\left(1-\frac{t}{t_{max}}\right) \tag{8}\]

In Equation (8), \(x\) is the constant, \(t\) represents current iterations and \(t_{max}\) is the maximum iteration count. Further, let \(b_{*}^{t+1}=arg-min\Big{(}f_{a_{i}^{t+1}}\Big{)}\); this equation employs acquisitive searching for attaining \(f_{a_{i}^{t+1}}\), which is represented via Equation (8),

\[ \begin{array}{c}a_{*}^{t+1}=b_{*}^{t+1}\qquad f\big{(}b_{*}^{t+1}\big{)}<f \big{(}a_{*}^{t}\big{)}\\ a_{*}^{t}\text{ otherwise}\end{array}  \tag{9}\]

Furthermore, let \(\,t=t+1\), and the process (in Equations (6)-(8)) repeats until the termination condition is attained. The overall procedure of SCA is given in Pseudocode 2. The initial phase involves the initialization of search agents. With the use of the objective function, search agent evaluation is undertaken. With the use of Equation (8), the ideal solution is updated and the control parameter \(d_{1}\) is updated. With the use of Equation (6), the positions of search agents are updated. This process continues until the maximum iterations are reached and the ideal solution is attained. Periodically, SCA faces issues such as getting stuck in a local area in the search space. This impacts the computational exertion that is required in searching for the ideal solution within the search space. The above issues could be resolved via enhancements to standard SCA that in turn enhance the SCA performance. On the contrary, WOA also possess disadvantages such as easy localization and low convergence. Hence, the present study endeavors to resolve these issues via several enhancements so as to procure better accuracy. With the exclusive optimization standard of SCA, this study intends to combine WOA and SCA so as to limit the demerits of both via hybridization, thereby attaining OWSA. As hybridization involves an enhancement of optimization methodologies, the present research proposes a OWSA wherein operators from certain methods are combined with other operators from supplementary methods so as to generate efficient and reliable results. In the current study, the OWSA is proposed for the optimal selection of features. It possesses the greatest ability to enhance the exploration phase. The overall process of optimal feature selection using OWSA is shown in Figure 2.

As depicted in Figure 2, all the whales are randomly set. Following this, the search agents are evaluated with the use of the objective function. Subsequently, the destination location is updated. Then, the fitness computation is performed. A position update is performed with SCA. Finally, the global optimal solution is attained. The overall sequence is presented in Pseudocode 3.