## 3 Design of HCOA-SACDC Model
### HCO-Based Parameter Optimization  
In the final phase, the HCO algorithm is exploited to adjust the parameters included in the SAE, thus increasing detection performance [21]. The cuckoo optimization algorithm (COA) is a popular optimization method, and it is the strongest one. It is inspired by the actions of a cuckoo bird. They can lay their eggs in the nests of other birds. Some limits are described, and it is optimized to manage various problems, for example, energy dispatch, controller parameters, job shops, cluster computing, system cost, and obtainability. In our work, the COA is improved to resolve system consistency optimization using heterogeneous components and is renamed the HCO algorithm. The probable solution can be created as nests, and the eggs are laid in the nests of different species. This is described below.  
Step 1. Initialize the parameters, including the input of the highest cuckoo generation \(N_{gen}\) and the number of nests \(M\) to be considered.  
Step 2. Create the nest. The nest can be created as follows:  
\[\left\{\begin{array}{l}Nest_{1}(r,\ n)=(r_{1},\ r_{2},\ \ldots,r_{m},\ n_{1},\ n_{2},\ \ldots,n_{m})\\ Nest_{2}(r,\ n)=(r_{1},\ r_{2},\ \ldots,r_{m},\ n_{1},\ n_{2},\ \ldots,n_{m})\\ \vdots\\ Nest_{M}(r,\ n)=(r_{1},\ r_{2},\ \ldots,r_{m},\ n_{1},\ n_{2},\ \ldots,n_{m}) \end{array}\right. \tag{5}\]  
where \(Nest(n,\ r)\) represents a collection of probable solutions.  
Step 3. The limitation is accomplished through the succeeding penalty function:  
\[\widetilde{R}_{s}(r,\ n)=R_{s}(r,\ n)+\varphi_{1}\cdot\text{Max }\{0,\ g_{1}(r,\ n)-V\}+|\varphi_{2}\]  
\[\text{Max }\{0,\ g_{2}(r,\ n)-C\}+\varphi_{3}\cdot\text{Max }\{0,\ g_{3}(r,\ n)-W\} \tag{6}\]Step 4. The cuckoo's egg can be placed according to the novel COA:  
\[ELR=\alpha\times\frac{Number\ of\ current\ cuckoo^{\prime}s\ eggs}{Total\ number\ of\ eggs}\times(V_{hi}-V_{low}) \tag{7}\]  
where \(ELR\) symbolizes the laying radius; \(\alpha\) represents an integer value; and \(V_{hi}\) and \(V_{low}\) represent the upper and lower limits, respectively.  
Step 5. The cuckoo's egg is introduced by 3 dissimilar hosts and dissimilar possibilities. Consequently, the cuckoo egg contains 3 dissimilar possibilities to successfully develop and characterize \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) [0%, 100%], called host quality. This value is subjectively completed by every single generation. Henceforth, the nest is detached into 3 groups, namely, \(M_{1}\), \(M_{2}\), and \(M_{3}\), and these values are subjective. Host quality can be described as follows:  
\[\left\{\begin{aligned} & M_{1}\ \text{{\it nests with}}\ \sigma_{1},&\qquad\text{where}\ M_{1}\in\{M\}\\ & M_{2}\ \text{{\it nests with}}\ \sigma_{2},&\qquad\text{where}\ M_{2}\in\{M-M_{1}\}\\ & M_{3}\ \text{{\it nests with}}\ \sigma_{3},&\qquad\text{where}\ M_{3}\in\{M-M_{2}-M_{1}\} \end{aligned}\right. \tag{8}\]  
Step 6. The optimal generation of cuckoo travels to alternative habitats; viz., the optimum solution existing in the forthcoming generation is used to enhance the search solution.  
Step 7. Iterate Steps 2-6 until the number of generations \((N_{gen})\) is accomplished.  
The HCO algorithm computes a fitness function to accomplish better classification performance. It derives a positive integer to characterize the solution candidate. The minimization of the classification error rate is considered as the fitness function, as given in Equation (9). The best solution has a reduced error rate, and the worst solution has an improved error rate (Algorithm 1).  
\[fitness(x_{i})=ClassifierErrorRate(x_{i})=\frac{number\ of\ misclassified\ samples}{Total\ number\ of\ samples}\times 100 \tag{9}\]  
```
Input: Parameter initialization: \(M\), \(N_{gen}\) Begin While \(z\leq N_{gen}\) Produce the nests using Equation (5) Determine the fitness value Carry out the egg laying Carry out the chick stage Migrate cuckoos End while Output: Report optimal solutions End
```  
**Algorithm 1**Pseudocode of HCO algorithm