## 4 Experimental Results
### Performance Evaluation
#### 4.2.4 Detailed Connection Analysis  
The calculation of drone-to-drone and station-to-drone distances also highlights the superiority of the hybrid approach. The minimum connection distances achieved for the PSO, GA, and hybrid approaches were 0.27 km, 0.14 km, and 0.12 km, respectively, which shows that the hybrid approach maintains a strong network by keeping the drones near one another. The maximum connection distances obtained were 2.87 km for PSO, 2.99 km for GAs, and 2.94 km for hybrid, confirming that the hybrid method does not compromise  
Figure 5: Comparison of average connection distance. Figure 6: Comparison of fitness values across methods. on connectivity while optimizing deployment. The connectivity visualization in Figures 7 and 8 illustrates the drone deployment and the corresponding connection links in each approach, demonstrating the stability of the network. The PSO-based deployment illustrates a quite uniform distribution of drones across the border and center of the mission area. Fixed-wing UAVs are placed mostly at the outer boundaries to provide maximum area coverage, while multirotors provide network connectivity in the inner area. The layout provides a good coverage of 25.01 km\({}^{2}\), but the 1.74 km average distance of communication indicates a somewhat loose network, which may translate to increased communication-related energy consumption during actual deployment. Compared to the PSO approach, the GA configuration coverage is slightly reduced at 24.62 km\({}^{2}\). Fixed-wing UAVs appear to be denser with a greater overlap and reduced boundary use. Despite this, the mean connection distance falls to 1.69 km, indicating greater energy efficiency over the PSO variant but with a loss of overall coverage. As seen in Figure 9, the hybrid solution of a GA and Q-learning gives the best configuration among the three. The solution has the highest coverage area (26.57 km\({}^{2}\)) with a smaller average connection distance of 1.59 km. The fixed-wing UAVs encircle the operational area effectively, and the multirotors create stable communication chains inside. Figure 8: Drone network configuration obtained using the genetic algorithm. Figure 7: Spatial deployment and connection topology generated by the PSO algorithm. This deployment represents the best balance between spatial coverage and communication efficiency, and it indicates the strength of the adaptive refinement in the hybrid model under deployment evolution. Table 2 presents a full breakdown of the connection statistics for each optimization technique. It identifies both the minimum and maximum observed connection distances in the drone networks, along with the average connection distances and the number of connections. A lower minimum distance, along with a satisfactory spread of the number of connections, is critical to maintaining network stability while optimizing coverage. Table 3 compares the overall performance of various optimization techniques based on three important parameters: area covered, number of connections, and average connection distance. The hybrid approach is seen to perform better by having the largest area covered while maintaining a maximum number of connections and average connection distances at a minimum. This indicates that the hybrid approach optimizes the positioning of drones effectively to enhance connectivity efficiency and maximize the area covered. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **PSO** & **GA** & **Hybrid** \\ \hline Minimum Distance (km) & 0.27 & 0.14 & 0.12 \\ \hline Maximum Distance (km) & 2.87 & 2.99 & 2.94 \\ \hline Average Distance (km) & 1.74 & 1.69 & 1.59 \\ \hline Total Connection & 19.0 & 21.0 & 20.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Connection statistics comparison.