# Security Situation Assessment for Terminal Area Control System Operation Based on BN-ISSA-ELM
## 2 Materials and Methods
### ISSA-ELM Risk Probability Prediction Model
#### 2.2.2 Improvements to the Sparrow Search Algorithm  
Although the Sparrow Search Algorithm (SSA) has credibility for being highly stable, accurate, and fast to converge, there is still the need for improvement in order to increase the cooperative optimization amongst scouts, joiners, and finders. This study presents multiple approaches to enhance the SSA. The OBL-improved SSA increases the diversity of search directions during the search process due to the introduction of the inverse solution, which enables the algorithm to approach the optimal solution faster. A set of better parameters can be found in a shorter time, which can quickly construct an efficient prediction model and provide timely decision support for safety management. The positive cosine algorithm and nonlinear sine learning factor are introduced to adjust the judgment conditions for updating the position of joiners, enhance the collaborative search capability among sparrows, and improve the search efficiency of the algorithm, so that the ISSA can mine the potential laws and features in the security assessment data of the terminal area more efficiently and improve the performance of the model. Introducing the Levy flight strategy, through the alternation of high-frequency short-distance exploration and low-frequency long-distance exploration,  
Figure 4: The ELM network architecture. and using different lengths of step lengths to start the search in local and global searches, avoiding the focus on the characteristics of the local posture elements and the neglect of other potentially important elements, allows for the more comprehensive assessment of the impact of each posture element on the security, and enables the model to better adapt to the complex and changing environment and the data characteristics of the security assessment of the terminal area, thereby improving the accuracy of the model. 1. Population Initialization Improvement  
An opposite population was established using the Opposite-Based Learning (OBL) technique based on the original population [25]. We then selected the next generation from the population that performed better. During selection, the OBL always selects the first person in the population who is closest to the ideal solution. Every iteration brings the population one step closer to the ideal solution, increasing the rate at which each individual converges. Assuming a d-dimensional space \(\mathrm{P}=(\mathrm{x1,x2,\ldots xd})\), the opposite population is represented as \(\mathrm{P^{\prime}}=\left(\mathrm{x1^{\prime},x2^{\prime},\ldots xd^{\prime}}\right)\), where  
\[\mathrm{xi^{\prime}}=\mathrm{MINi}+\mathrm{MAXi}-\mathrm{xi} \tag{1}\]  
In Equation (1), xi represents the opposite solution, and \(\mathrm{xi}\in\left[\mathrm{MINi,MAXi}\right],\ \mathrm{i=1,2,\ldots d}\); \(\mathrm{MINi,\ MAXi}\) are the minimum and maximum values for the i-th dimension across all of the initial solutions. 2. Improvement of Finder Search Capability  
In the Sparrow Search Algorithm (SSA), the finder has a pivotal role. If the finder fails to update its position during the search phase and becomes ensnared in a local optimum, the entire population may also converge to a local optimum. To improve the algorithm's precision, it is crucial to permit the SSA to utilize higher values in the initial phases of the search for global exploration while permitting lower values in the subsequent phases to promote local exploitation. To achieve this, the position update method for the finder incorporates the Sine-Cosine Algorithm and a nonlinear sine learning factor to enhance the search capability of the finders [26]. The updated formula for the finder's position is as follows:  
\[\mathrm{X_{ij}^{t+1}}=\left\{ \begin{aligned} &(1-\omega)\cdot\mathrm{X_{ij}^{t}}+ \omega\cdot\sin(\tau 1)\cdot\left|\mathrm{r2\cdot X_{best}-X_{ij}^{t}} \right|,\ \mathrm{R2<ST}\\ &(1-\omega)\cdot\mathrm{X_{ij}^{t}}+\omega\cdot\cos(\tau 1)\cdot \left|\mathrm{r2\cdot X_{best}-X_{ij}^{t}}\right|,\ \mathrm{R2\geq ST}\end{aligned} \right.