# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 3 Model Performance Verification  
### Performance Verification of ISSA  
We have selected five benchmark functions to evaluate the effectiveness of the Improved Sparrow Search Algorithm (ISSA). Table 3 describes the specifications of these benchmark functions. We apply the ISSA, SSA, PSO, WOA, and GSA algorithms to five peaked single-dimensional basic functions to observe whether they can effectively search toward the global optimal solution. The high-dimensional single-peak function has only one global optimal point, and there is no local extreme point, which can be a good test of the convergence speed of the function. During the search process, the distance between the solution obtained by each iteration of the algorithm and the global optimal solution is recorded, and the mean and standard deviation are taken as evaluation indices. Table 4 shows the mean and standard deviation of the results after 30 and 100 executions of the five optimization algorithms on the five benchmark functions. The results show that the mean value obtained by the ISSA is closer to the theoretical optimal solution of zero and the standard deviation decreases. This indicates that the ISSA exhibits enhanced search capability in identifying the optimal solution. In addition, as shown in Table 4, increasing the number of runs from 30 to 100 leads to stable results for the ISSA, resulting in high-quality solutions. This observation validates that the ISSA has higher stability. In summary, the ISSA algorithm has commendable optimization accuracy and stability, making it a viable approach for solving optimization challenges. \begin{table}
\begin{tabular}{c c} \hline
**SA(T)** & **Description** \\ \hline
0 & No risk; the safety situation factors show no dynamic fluctuations. \\ \((0,1]\) & Overall safety is controlled; safety situation factors show no dynamic fluctuations. \\ \((1,2]\) & Overall safety is controlled; a small number of safety situation factors exhibit slight dynamic fluctuations. \\ \((2,3]\) & Overall safety is basically controlled; a small number of safety situation factors exhibit moderate dynamic fluctuations. \\ \((3,4]\) & Some terminal areas exhibit risks; certain safety situation factors demonstrate significant dynamic fluctuations. \\ \((4,5]\) & Some terminal areas are at risk of losing control; several safety situation factors exhibit substantial dynamic fluctuations. \\ \((5,6]\) & Some terminal areas are out of control; multiple safety situation factors exhibit significant dynamic fluctuations. \\ \hline \end{tabular}
\end{table} 
Table 2: Explanation of the safety situation values. \begin{table}
\begin{tabular}{c c} \hline
**Serial Number** & **Benchmark Function** \\ \hline
1 & \(\text{F1}(X)=\sum\limits_{i=1}^{n}X_{i}^{2}\) \\
2 & \(\text{F2}(X)=\sum\limits_{i=1}^{n}|X_{i}^{2}|+\prod\limits_{i=1}^{n}|x_{i}|\) \\
3 & \(\text{F3}(X)=\sum\limits_{i=1}^{n}\left(\sum\limits_{m=1}^{i}x_{m}\right)^{2}\) \\
4 & \(\text{F4}(X)=\max_{i}\{|x_{i}|,1\leq i\leq n\}\) \\
5 & \(\text{F5}(X)=\sum\limits_{i=1}^{n-1}\left[100\left(x_{i+1}-x_{i}^{2}\right)^{2} +\left(x_{i}-1\right)^{2}\right]\) \\ \hline \end{tabular}
\end{table} 
Table 3: Benchmark function. To validate the advantages of the model, and to make the model more complete and more convincing, a single iteration time comparison between the ISSA and SSA was conducted. Under the same hardware environment and test problem, the ISSA and SSA were run separately, and the time they took to perform one iteration with the same size population was recorded. We compared the computational efficiency of the ISSA with SSA per iteration run time and found that the ISSA ran more efficiently than the SSA, as shown in Table 5.  
The improved computational efficiency of the ISSA has applicability to the real-time security applications of terminal area control systems. The ISSA shows a significant improvement in computational efficiency compared to the SSA, which makes it capable of