## 3 The Proposed Model
### Feature Selection: EKHO Algorithm  
In the presented DWOML-RWD technique, the EKHO algorithm is used to perform the feature selection process. The KHO algorithm is inspired by the herding behavior of individual krill that is expressed by random diffusion, induced movement, and foraging activity [18]. For weight optimization, we employed the KHO technique; it is able to resolve optimization problems effectively. It is the more commonly employed heuristics optimization technique. This method imitates the krill individual (_KI_) behavior in KH. It contains two chief goals: (1) attaining food and (2) augmenting krill density. The position of _KI_ can be affected by the following factors: (1) random diffusion; (2) movement inspired by other _KI_; and (3) foraging activity. Hence, the krill position is formulated using the Lagrangian model in the following:  
\[\frac{dUli}{dt}=Mo_{i}+Fa_{i}+Df_{i}, \tag{1}\]  
where:  
\(Mo_{i}\)--movement guided by other _KI_. \(Fa_{i}\)--foraging movement. \(Df_{i}-i\)--_KI_ physical diffusion. The steps are shown below. Step 1: the motion of the direction of _KI_ can be defined using repulsive swarm density, local swarm, and target swarm as follows:  
\[Mo_{i}^{new}=Ma^{m}\alpha_{i}+\omega_{n}M_{i}^{old}, \tag{2}\]  
where:  
\(Ma^{m}\)--maximum induced speed. \(\omega_{n}\)--motion inertia weight within \([0,1]\). \(M_{i}^{old}\)--last motion induced. \(\alpha_{i}\) is estimated by Equation (3)  
\[\alpha_{i}=\alpha_{i}^{local}+\alpha_{i}^{target} \tag{3}\]  
Figure 1: Block diagram of DWOML-RWD approach. \(\alpha_{i}^{local}\) denotes the local effects of a neighbor of \(i\)-th individuals, and \(\alpha_{i}^{target}\) represents the optimum solution direction from \(i\)th individuals. \[\alpha_{i}^{target}=I_{b}K_{ib}X_{ib}, \tag{4}\]  
In Equation (4), \(I_{b}\) indicates the coefficient and determined \(\alpha_{i}^{target}\) for \(i\)-th individuals. \[I_{b}=2\bigg{(}\frac{ra+1}{I_{ma}}\bigg{)}, \tag{5}\]  
where \(ra\) refers to the random number within \((0,1)\). Step 2: The foraging behavior can be estimated by  
\[Fa_{i}=Sp_{f}\gamma_{i}+\omega_{f}Fa_{i}^{old}, \tag{6}\]  
where  
\[\gamma_{i}=\gamma_{i}^{best}+B_{i}^{best}, \tag{7}\]  
Here, \(Sp_{f}\) denotes the foraging speed, \(\omega_{f}\) shows the inertia weight for foraging, and \(B_{i}^{best}\) represents the optimal solution. Step 3: The physical diffusion of \(KI\) is an arbitrary method, and the movement related to \(Df_{i}\) and \(\delta\) is evaluated in the following expression:  
\[Df_{i}=Df_{m}\delta, \tag{8}\]  
In Equation (8), \(Df_{f}\) represents the maximal diffusion speed, and \(\delta\) denotes the random directional vector and arrays of random numbers in \((-1,1)\). The movement of the krill swarm determines the procedure that identifies the optimum fitness. Hence, the \(KI\) location can be given as follows:  
\[U_{i}(t+\Delta t)=U_{i}(t)+\Delta t\frac{dU_{i}}{dt}. \tag{9}\]  
The variable \(\Delta t\) is crucial and regarded as a scaling factor of the speed vector. Hence, it should be modified regarding the optimization problem. The value of \(\Delta t\) fully depends on the search space. The EKHO algorithm was designed by using the DOBL concept. The OBL method was utilized for generating a unique opposition solution to the present solution [19]. It tries to produce the optimum solution, which leads to the speed rate of the convergences being enhanced. The opposite \(\big{(}X^{0}\big{)}\) of a provided real number \((X\in[U,\ L])\) is measured as:  
\[Mo_{i}^{new}=Ma^{m}\alpha_{i}+\omega_{n}M_{i}^{old}, \tag{2}\]0  
The opposite point, \(X=[X_{1},\ X_{2},\dots,\ X_{Dim}]\), is a point from \(Dim\)-dimensional searching space, \(X_{1}\), \(X_{2},\dots,X_{Dim}\in R\), and \(X_{j}[U_{j},L_{j}]\).