# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 4 Experimental Verification
### Parameter Settings
#### 4.3.2 The Results of the Bilevel Optimization Algorithm  
Using the bilevel optimization algorithm proposed in this paper, task assignment and path planning were performed for the task units in Section 4.3.1. A Pareto front was established between the two optimization objectives, revealing the trade-off between the total flight distance and the balance of flight distances among individual UAVs. This Pareto front was constructed by combining the Pareto solution sets from ten runs of the algorithm, forming a new and more comprehensive Pareto front, as shown in Figure 16.  
In the figure, the blue dots represent all potential solutions generated by the algorithm, the solid red dots identify the Pareto front points recognized by the algorithm, and the dashed red line indicates the Pareto front curve constructed by these Pareto front points. From the figure, it can be observed that as the iteration process progresses, the Pareto front points (solid red dots) gradually approach and stabilize near a certain curve, indicating that the algorithm can effectively approximate the true Pareto front. Furthermore, the Pareto front curve represented by the dashed red line exhibits smooth and continuous characteristics, further confirming the convergence trend of the solution set of the algorithm. Among the Pareto-optimal solutions, the solution with the minimum F2 value was selected, and the corresponding flight sequence diagrams for this particular solution were generated (as shown in Figures 16-20). \begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Parameter Value** \\ \hline Population size & 50 \\ Maximum iterations & 100 \\ Crossover probability of partial matching crossover operator & 0.7 \\ Reversal probability & 0.5 \\ \hline \end{tabular}
\end{table} 
Table 5: Parameter settings for the optimization of the final landing hive of UAVs. Figure 15: Unit task segmentation in 284 task point scenarios. Figure 16: Pareto front obtained by GA-NSGA-II algorithm in 284 task points. Figure 17: U1 flight sequence obtained by GA-NSGA-II algorithm in 284 task points. Figure 18: U2 flight sequence obtained by GA-NSGA-II algorithm in 284 task points. In Figure 17, the red line delineates the flight route of UAV U1. Departing from the initial hive f3, UAV U1 navigates to the first task on Route 1, successively inspecting each task on this route until completion. After finishing Route 1, U1 flies to hive f1 for a battery swap. Post-replacement, U1 moves to the initial task of Route 2 and continues inspections following the sequence of tasks numbered 1 through 6. Upon completing inspections of all tasks across the routes, U1 lands at hive f3, marking the end of the flight mission. In Figure 18, the yellow line depicts the flight path of UAV U2. Starting from the initial hive f5, UAV U2 heads to the first task on Route 1 and successively inspects each task on this route until the tasks are completed. After finishing Route 1, U2 flies to hive f5 for a battery replacement. Once the battery replacement is completed, U2 proceeds to the starting task of Route 2 and continues with the inspection tasks in the order of tasks numbered 1 through 6. Ultimately, after completing the inspection of all tasks on all routes, U2 lands at hive f4, concluding the entire flight mission. In Figure 19, the green line represents the flight path of UAV U3. UAV U3 departs from the starting hive f1 and proceeds to the first task on Route 1, conducting inspections of each task on this route in sequence until the tasks are completed. After finishing Route 1, U3 returns to hive f3 for a battery replacement. Following the battery replacement, U3 flies to the starting task of Route 2 to continue inspections, executing the tasks in the order of  
Figure 19: U3 flight sequence obtained by GA-NSGA-II algorithm in 284 task points. Figure 20: U4 flight sequence obtained by GA-NSGA-II algorithm in 284 task points. numbers 1 through 6.