## 3 The Proposed Model
### Process Involved in ECOA-FS Technique  
Initially, the IMFL-IDSCS technique used the ECOA-FS approach for choosing optimal features. COA is a mathematical model that can be reliant on intelligent diversity [26]. Chase, drive, attack, and block are the distinct strategies of chimps that can realize obstacles, the attacker, the driver, and the chaser. These hunting behaviors are concluded in two stages. Exploration and exploitation are the two stages of COA. The initial stage comprises  
Figure 1: Workflow of IMFL-IDSCS system. chasing, driving, and blocking the prey. The next phase has attacked the prey. At the same time, the driving and chasing behaviors are illustrated in Equations (1) and (2):  
\[d=\left|c\cdot x_{prey}(t)-m\cdot x_{chimp}(t)\right| \tag{1}\]  
\[x_{chimp}(t+1)=x_{prey}(t)-a\cdot d \tag{2}\]  
Now, \(X_{prey}\) stands for the vector of prey location, \(x_{chimp}\) indicates the vector of chimp place, \(t\) represents the number of current iterations, and \(a,c\), and \(m\) imply the coefficient vector. This is accomplished in Equations (3)-(5):  
\[a=2\cdot fc\cdot r_{1}-fc \tag{3}\]  
\[c=2\cdot r_{2} \tag{4}\]  
\[m=chaotic\_value \tag{5}\]  
Whereas \(fc\) indicates the non-linear decrease in 2.5 to 0, \(r_{1}\) and \(r_{2}\) indicate arbitrary value ranges from zero to one, and \(m\) denotes chaotic vector. The dynamic coefficient \(fc\) prefers dissimilar curves and slopes. Hence, the chimps used different abilities to search for prey. They upgraded the location dependent upon other chimps, and the mathematical modeling can be given in Equations (6)-(8):  
\[ \begin{array}{l}d_{Attacker}=\left|c_{2}x_{Attacker}-m_{1}x\right|\\ d_{Barrier}=\left|c_{2}x_{Barrier}-m_{2}x\right|\\ d_{Chaser}=\left|c_{3}x_{Chaser}-m_{3}x\right|\\ d_{Driver}=\left|c_{4}x_{Driver}-m_{4}x\right|\\ \end{array}  \tag{6}\]  
\[ \begin{array}{l}\chi_{2^{-3}Barrier}-_{2}(a_{Barrier})\chi_{1^{-3}A_{Alister }-_{1}(a_{Alister})}\\ x_{4}=x_{Driver}-a_{4}(d_{Driver})x_{3}=x_{Chaser}-a_{3}(d_{Chaser})\\ \end{array}  \tag{7}\]  
\[ \begin{array}{l}x(t+1)=\frac{x_{1}+x_{2}+x_{3}+x_{4}}{4}\\ x_{1}=x_{Attacker}-a_{1}(d_{Attacker})\\ x_{2}=x_{Barrier}-a_{2}(d_{Barrier})\\ x_{3}=x_{Chaser}-a_{3}(d_{Chaser})\\ x_{4}=x_{Driver}-a_{4}(d_{Driver})\\ \end{array}  \tag{8}\]  
In ECOA, the extremely disruptive polynomial mutation was a revised edition of the polynomial mutation method. It resolves the constraint that the polynomial mutation method fell as the local optima when the parameter was near the boundary, as shown in Equations (9) and (12):  
\[\delta_{1}=\frac{\chi_{i}-lb}{ub-lb} \tag{9}\]  
\[\delta_{2}=\frac{ub-x_{i}}{ub-lb} \tag{10}\]  
\[\delta_{k}=\left\{ \begin{array}{l}[(2r)+(1-2r)*(1-\delta_{1})^{\eta_{m}+1}] ^{\frac{1}{\eta_{m}+1}-1},r\leq 0.5\\ 1-[2(1-r)+2(r-0.5)*(1-\delta_{2})^{\eta_{m}+1}]^{\frac{1}{\eta_{m}+1}},\; otherwise\\ \end{array} \right. \tag{11}\]  
\[x_{i}=x_{i}+\delta_{k}(ub-lb) \tag{12}\]  
Whereas \(ub\) and \(lb\) determine the upper and lower limits of search spaces, \(r\) indicates the arbitrary value ranges within [0, 1] and \(\eta_{m}\) indicates the distribution exponential that can be a non-negative number. Unlike the traditional ECOA, the solution update occurs in the search region in the direction of the continual value position. However, in BECOA, the search region is determined as the \(n\) dimension Boolean lattice. Moreover, the solution is upgraded through the corner of the hypercube. In addition, for choosing features, 1 characterizes the feature selection, or 0. Furthermore, the BECOA derives a fitness function from determining solutions for retaining tradeoffs amongst a pair of objectives, as defined in Equation (13):  
\[fitness=\alpha\Delta_{R}(D)+\beta\frac{|Y|}{|T|} \tag{13}\]  
\(\Delta_{R}(D)\) shows the classifier's error, \(|Y|\) indicates the subset size, and \(|T|\) represents the overall features present in the data.