# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 4 Experimental Verification
### Comparison of Experimental Results
#### 4.4.2 Comparison with Other Power Grid Scenarios  
To verify the effectiveness of the GA-NSGA-II bilevel optimization algorithm, we selected a new scenario with sparsely distributed transmission towers for validation, in contrast to the previously dense scenario. The new scenario is also located in Zhejiang Province, geographically situated between 30.34\({}^{\circ}\) N and 30.47\({}^{\circ}\) N latitude and between 119.75\({}^{\circ}\) E and 120.15\({}^{\circ}\) E longitude. It consists of five transmission lines, 406 towers, and four UAV battery swap hives from four different task packages, as shown in Figure 32.  
Following the algorithm's procedure, after performing unit task segmentation in the power grid scenario, we obtained 10 task fragments, as shown in Figure 33. In the figure, each unit task (ftask) is represented by a unique color and symbol and is numbered from 0 to 9. Subsequently, we obtained the Pareto front of the total flight distance and the equilibrium of flight distances among individual UAVs through multiple runs, as shown in Figure 34.  
Among the Pareto-optimal solutions generated from the new scenario data, the solution with the minimum F2 value was still selected. For this particular solution, the corresponding flight sequence diagrams were generated (as shown in Figures 35-38). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Number** \\ **of UAVs** \\ **(units)** \\ \end{tabular}  & \begin{tabular}{c} **Total** \\ **Flight** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **U1 Flight** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **U2 Flight** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **U3 Flight** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **U4 Flight** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **U5 Flight** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Execution** \\ **Time(s)** \\ \end{tabular}  \\ \hline GA-GA & 5 & 150,962 & 28618 & 59879 & 12024 & 36235 & 14206 & 17354.78 & 4374.43 \\ \hline \ \begin{tabular}{c} Task- \\ adaptive \\ clustering \\ \end{tabular}  & 5 & 151,824 & 50302 & 26573 & 30929 & 32218 & 11802 & 12330.45 & 11.32 \\ \hline \multirow{7}{*}{ \begin{tabular}{c} GA- \\ NSGA-II \\ \end{tabular}  } & 5 & 150,788 & 6528 & 31933 & 50,618 & 23,753 & 37,956 & 14,710.35 & 4642.33 \\ \cline{2-11}  & 5 & 150,852 & 22,946 & 28,417 & 42,267 & 16,330 & 40,892 & 10,  
Figure 34: Pareto front obtained by GA-NSGA-II algorithm in 406 task points. Figure 32: Top view of the realistic power grid scene with 406 task points. Figure 33: Unit task segmentation in 406 task point scenarios. Figure 35: U1 flight sequence obtained by GA-NSGA-II algorithm in 406 task points. Figure 36: U2 flight sequence obtained by GA-NSGA-II algorithm in 406 task points. Figure 37: U3 flight sequence obtained by GA-NSGA-II algorithm in 406 task points. In Figure 35, UAV U1 takes off from hive f3, first inspects the towers on the route marked as 1, then returns to f3 for battery swapping, and continues the inspection in the order of 1 to 4, finally landing at hive f4. In Figure 36, UAV U2 takes off from hive f4, first inspects the towers on the route marked as 1, then returns to f4 for battery swapping, and continues the inspection in the order of 1 to 4, finally landing at hive f3. In Figure 37, UAV U3 takes off from hive f1, first inspects the towers on the route marked as 1, then returns to f1 for battery swapping, and continues the inspection in the order of 1 to 4, finally landing at hive f4. In Figure 38, UAV U4 takes off from hive f2, first inspects the towers on the route marked as 1, then returns to f2 for battery swapping, and continues the inspection in the order of 1 to 4, finally landing at hive f1. From the results shown in Figures 35-38, we can observe that the flight routes of UAVs U1 to U4 are very close in distance.