# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Settings of Ant Colony Algorithm Parameters  
In the ant colony algorithm, the selection of different parameters has an important impact on the performance of the algorithm.  
A larger information prime weight factor \(\alpha\) will weaken the ability to search for other paths in the ant colony algorithm, while the smaller \(\alpha\) value can easily cause the ant colony algorithm to fall into a locally optimal solution. Similarly, a larger inspiration factor \(\beta\) will weaken the role of pheromone in the algorithm process, leading to the local optimal solution; and the smaller \(\beta\) will turn the ant colony algorithm into a simple random search. Therefore,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Total number of satellite nodes & 66 \\ Number of malicious satellite nodes & 16 \\ Data packet length & 100 B \\ Ant colony population size & 100 \\ Number of ant colony iterations & 500 \\ Trust vector length passed to VAE network \(l_{w}\) & 10 \\ Direct trust vector adjustment parameter \(v\) & 0.5 \\ Fixed threshold \(r\) & 0.8 \\ Trust evaluation model execution cycle(s) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental simulation parameters.  
the appropriate value of \(\alpha\) and \(\beta\) should be determined by experiments. According to the experimental results (Figure 4), we found that when \(\alpha=1\), \(\beta=2\), the ant colony algorithm has the minimum number of iterations, so we chose this set of parameter values.  
In addition, the value of the volatilization coefficient of the pheromone \(\rho\) in Figure 5 directly affects the global search ability and convergence speed of the ant colony algorithm. The larger value will lead to the rapid volatilization of the searched path, increasing the probability of the ants choosing the duplicate path. The smaller \(\rho\) value will make the pheromone play slowly, thereby weakening the convergence of the algorithm.  
The value of the pheromone volatilization coefficient is determined by experiments. We find that when \(\rho=0.5\), the ant colony algorithm can achieve the optimal solution with the least iteration. Therefore, in the experiments of this article, the parameters of the ant colony algorithm are \(\rho=0.5\).  
The settings of the ant colony algorithm's hyperparameters, denoted as \(\alpha\) and \(\beta\), correspond to Formulas (18) and (20) mentioned earlier. The parameter \(\alpha\) determines the weight of the product of pheromones and the security evaluation factor, while the parameter \(\beta\) determines the weight of the heuristic factor. The parameter \(\rho\) determines the magnitude of the pheromone evaporation factor. In the ant colony algorithm, when ants choose the next hop, they rely on Formula (18) to calculate probabilities. Ants are more inclined to search for paths with larger pheromone levels and higher security. Without the constraint of \(\beta\), ants would disregard the distances between nodes during path selection.  
Figure 4: The values of pheromone \(\alpha\) and heuristic factor \(\beta\).  
Figure 5: The value of volatile factor \(\rho\).  
This could lead to most ants following certain fixed routes, potentially missing the optimal path. \(\beta\), however, takes into account the distances between nodes. Consequently, ants have the opportunity to explore alternative paths during path selection, enabling the discovery of superior routes.  
Furthermore, \(\rho\) primarily determines the update of ant pheromones. As indicated by Formula (20), the ant pheromones are not retained permanently on traversed paths; their evaporation is determined by the magnitude of \(\rho\). Setting \(\rho\) too large or too small is unreasonable, as explained in the preceding text. Therefore, experimental data suggest that when \(\alpha=1\), \(\beta=2\), and \(\rho=0.5\), the ant colony algorithm achieves optimal performance, ensuring the reliability of satellite network routing.