# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 6 Experimental Results and Discussion
### Experiments on Training Scenarios
#### 6.2.1 Comparing the Performance of Evolved Solutions  
The simulation experiments ran 10 times with different random seeds to evolve solutions considering one-hop and two-hop communication between UAVs. Figure 7 and Table 2 show the fitness obtained using \(Best_{1h}\), \(Best_{2h}\), and ATRI [15] on the four training scenarios with 156 UAVs and 200 users, where \(Best_{1h}\) and \(Best_{2h}\) are the best solutions evolved using GA considering one-hop and two-hop communication respectively. Table 2 shows that, on \(T_{r1}\), the \(Best_{2h}\) performance is 14.2% higher than \(Best_{1h}\), and 20.62% higher than ATRI. In addition, the performance of \(Best_{1h}\) is 5.62% higher than ATRI. Similar performance improvements are observed on \(T_{r2}\), \(T_{r3}\), and \(T_{r4}\). On average, the \(Best_{2h}\) performance is 18.90% higher than \(Best_{1h}\) and 41.30% higher than ATRI. Similarly, the \(Best_{1h}\) fitness is 18.88% more than ATRI. Figure 6: UAV deployment within an AOI with 10 users (blue dots). (**a**) shows initial UAV positions covering the area, (**b**) shows the final UAV positions focusing on user locations and bandwidth sharing, and (**c**) compares the three search algorithms. Close inspection indicates that the fitness of \(Best_{2h}\) is higher because networks deployed using \(Best_{2h}\) provide more bandwidth coverage to users compared to others and have larger numbers of AUs as shown in Figure 8. Note that fitness is the combination of the percentage bandwidth coverage and percentage of active UAVs. Figure 8 compares (a) the bandwidth served and (b) the number of active UAVs in networks deployed using the \(Best_{2h}\), \(Best_{1h}\), and ATRI in four scenarios, respectively. The larger the number of AUs, the smaller the per AU data rate provided to users, leading to less energy consumption and increasing the longevity of the network. Figure 8c shows that the per AU data rate for \(Best_{2h}\) is smaller than the other two, indicating a higher number of AUs and thus a higher fitness. The figure also shows that the per AU data rate for \(Best_{1h}\) is smaller than ATRI, again indicating higher fitness than ATRI. These results provide evidence that the proposed genetic-algorithm-optimized network deployment approach is better than ATRI, the current best dynamic network deployment technique. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Users** & **Methods** & \(\mathbf{\mathcal{T}_{1}}\) & \(\mathbf{\mathcal{T}_{2}}\) & \(\mathbf{\mathcal{T}_{3}}\) & \(\mathbf{\mathcal{T}_{4}}\) \\ \hline \multirow{3}{*}{200} & ATRI & 1.60 & 1.12 & 1.34 & 1.47 \\  & GANet-1 hop & 1.69 & 1.56 & 1.59 & 1.71 \\  & GANet-2 hop & **1.93** & **1.95** & **1.97** & **1.94** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparing fitness obtained with 156 UAVs and 200 users on training scenarios. Figure 8: Comparing results on four training scenarios: (a) bandwidth served by deployed networks, (b) the number of AUs in the networks, and (c) per AU data rate. Figure 7: Comparing the best fitness obtained using GANet 1-hop, GANet 2-hop, and ATRI. Additional experiments were conducted by considering three-hop communication between UAVs, but no significant performance improvement was observed. To determine why ATRI's performance is inferior to GANet, UAVs distributions in the networks deployed using ATRI, \(Best_{1h}\), and \(Best_{2h}\) on the first training scenario (\(T_{r1}\)) are shown by Figure 9. The figure illustrates that solutions (\(Best_{1h}\), \(Best_{2h}\)) evolved using GA allow UAVs to move close to users that are distributed throughout the AOI (see Figure 9b,c) while maintaining connectivity with the command center. The evolved potential field parameters guide UAVs toward the area with more user concentration to serve them better. This leads to a higher number of AUs in the deployed network, better network capacity utilization, and higher fitness.