## 5 SU-CMO-Based Hybrid Classification
### SU-CMO Model  
The extant CMBO [26] model gives optimal solutions; still, it tolerates low accuracy. For the aim of overcoming the drawbacks of conventional CMBO, specific improvements were made. The steps in the SU-CMO model and flowchart (Figure 2) are given below.  
1. The initial population of \(B\) search agents is initialized.
2. The parameters of \(B\), \(B_{c},B_{m},T\) are initialized. Here, \(B\) is the count of members in the population matrix \(A\).
3. The initial population is created as per Equation (20).  
\[A=\begin{bmatrix}A_{1}\\ A_{2}\\ \vdots\\ A_{B}\end{bmatrix}_{B=m}=\begin{bmatrix}y_{1,1}&\cdots&y_{1,d}&\cdots&y_{1,m} \\ \vdots&\ddots&\vdots&\cdots&\vdots\\ y_{i,1}&\cdots&y_{i,d}&\cdots&y_{i,m}\\ \vdots&\cdot&\vdots&\ddots&\vdots\\ y_{B,1}&\cdots&y_{B,d}&\cdots&y_{B,m}\end{bmatrix}_{B=m} \tag{20}\]  
Here, \(y_{i,d}\) is the \(d^{th}\) problem variable.  
1. The fitness of the search agents is computed as per Equation (21).  
\[Obj=\min(Err) \tag{21}\]
2. Using Equations (22) and (23), update the sorted population matrix \(A^{S}\). Here, the \(i^{th}\) population of the sorted population matrix is denoted as \(y^{S}_{i,d}\). In addition, \(Obj^{S}\) is the sorted objective function-based vector.  
\[A^{S}=\begin{bmatrix}A^{S}_{1}\\ A^{S}_{2}\\ \vdots\\ A^{S}_{B}\end{bmatrix}_{B=m}=\begin{bmatrix}y^{S}_{1,1}&\cdots&y^{S}_{1,d}& \cdots&y^{S}_{1,m}\\ \vdots&\ddots&\vdots&\cdots&\vdots\\ y^{S}_{i,1}&\cdots&y^{S}_{i,d}&\cdots&y^{S}_{i,m}\\ \vdots&\cdots&\vdots&\ddots&\vdots\\ y^{S}_{B,1}&\cdots&y^{S}_{B,d}&\cdots&y^{S}_{B,m}\end{bmatrix}_{B=m} \tag{22}\]  
\[Obj^{S}=\begin{bmatrix}Obj^{S}_{1}\min(Obj)\\ Obj^{S}_{2}\min(Obj)\\ \vdots&\vdots\\ Obj^{S}_{B}\min(Obj)\end{bmatrix}_{B=1} \tag{23}\]Step 6: Using Equation (24), the mice population is chosen.  
\[M=\begin{bmatrix}M_{1}=X_{1}^{S}\\ \vdots\\ M_{i}=X_{i}^{S}\\ \vdots\\ M_{B_{m}}=X_{B_{m}=m}^{S}\end{bmatrix}_{B_{m}=m}=\begin{bmatrix}y_{1,1}^{S}& \cdots&y_{1,d}^{S}&\cdots&y_{1,m}^{S}\\ \vdots&\ddots&\vdots&\cdot&\vdots\\ y_{i,1}^{S}&\cdots&y_{i,d}^{S}&\cdots&y_{i,m}^{S}\\ \vdots&\cdot&\vdots&\ddots&\vdots\\ y_{B_{m},1}^{S}&\cdots&y_{B_{m},d}^{S}&\cdots&y_{B_{m},m}^{S}\end{bmatrix}_{B_ {m}=m} \tag{24}\]  
Step 7: Using Equation (25), the cat population is selected.  
\[C=\begin{bmatrix}C_{1}=X_{B_{m}+1}^{S}\\ \vdots\\ C_{i}=X_{B_{m}+j}^{S}\\ \vdots\\ C_{B_{c}}=X_{B_{m}+B_{c}}^{S}\end{bmatrix}_{B_{c}+m}=\begin{bmatrix}y_{B_{m}+1, 1}^{S}&\cdots&y_{B_{m}+1,d}^{S}&\cdots&y_{B_{m}+1,m}^{S}\\ \vdots&\ddots&\vdots&\cdot&\vdots\\ y_{B_{m}+j,1}^{S}&\cdots&y_{B_{m}+j,d}^{S}&\cdots&y_{B_{m}+j,m}^{S}\\ \vdots&\cdot&\cdot&\vdots&\ddots&\vdots\\ y_{B_{m}+B_{c},1}^{S}&\cdots&y_{B_{m}+B_{c},d}^{S}&\cdots&y_{B_{m}+B_{c},m}^{S} \end{bmatrix}; \tag{25}\]  
Step 8: Here, \(M,B_{m},M_{i},C,B_{c},C_{j}\) points to the mice population, count of mice, \(j^{th}\) mice, cat population, count of cats and the \(i^{th}\) cat, respectively.  
Step 9: The position update of cats is modeled as in Equation (26), where \(C_{j}^{new}\) new points to the new position of the \(j^{th}\) cat and \(C_{j,d}\) is the new value for \(d^{th}\) problem. In addition, the random value \(r\) is estimated randomly within the limit \([0,1]\). Here, I is computed as in Equation (27), where \(rand\) is a random integer.  
\[C_{j}^{new}=\left[C_{j,d}+r*\left(M_  
* If \(i=B_{m}\) then 1. if the above condition is satisfied, then check if \(t=T\). 2. if the above condition is not satisfied, then increase \(i\) by 1. 3. End if. 4. If \(t=T\), then the best solution acquired so far is returned. 5. If \(t\
eq T\), then increase \(i\) by 1 and move back to step 8. 6. Terminate.