# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 4 Experimental Verification
### Comparison of Experimental Results
#### 4.4.1 Comparison with Other Algorithms
In Figure 29, UAV U3 takes off from hive f1, 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 2, finally landing at hive f3. In Figure 30, UAV U4 takes off from hive f5, 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 5, finally landing at hive f1. In Figure 31, UAV U5 takes off from hive f2, 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 5, finally landing at hive f4. The GA-GA algorithm and the adaptive clustering optimization algorithm exhibit similar issues in their results. As shown in Figures 27-31, there is a significant disparity in flight distances among UAVs, which similarly leads to a large difference in the time required for each UAV to complete its mission. This imbalanced task assignment severely reduces the overall efficiency of the inspection process and fails to fully leverage the advantages of multi-UAV inspections. In order to further compare the performance of the task-adaptive clustering optimization algorithm, the GA-GA algorithm, and the GA-NSGA-II algorithm, we recorded the total flight distance, the flight distance of each UAV, and the corresponding runtime for all three algorithms. For the mission-adaptive clustering optimization algorithm and the GA-GA algorithm, we select the shortest flight distance from the 10 flight results and take it as the final representative result of the algorithm. Then, the results of the GA-NSGA-II algorithm which are superior to the task-adaptive clustering optimization algorithm and the GA-GA algorithm are selected for comparison. The specific data of the comparison are shown in Table 6.  
The results from Table 6 indicate that the GA-NSGA-II bilevel optimization algorithm demonstrates significant advantages in two key optimization objectives: the total flight distance and the balance of the UAV workload. Specifically, this algorithm is capable of identifying solutions that not only reduce the overall flight distance but also distribute the workload among UAVs more evenly. Compared with the task-adaptive clustering optimization algorithm and the GA-GA bilevel optimization algorithm, the GA-NSGA-II algorithm not only effectively decreases the total flight distance of the UAVs but also performs better in terms of workload distribution balance. In our comparative analysis, we selected results from the GA-NSGA-II algorithm that outperformed both the GA-GA bilevel optimization algorithm and the adaptive clustering optimization algorithm in terms of total flight distance and standard deviation. The results show that, compared to the GA-GA bilevel optimization algorithm, the GA-NSGA-II algorithm reduces the standard deviation of flight distances among UAVs by 41.91% to 84.63% and the flight distance of the longest UAV flight by 29.41% to 43.98%. When compared to the adaptive clustering optimization algorithm, the standard deviation of flight distances among UAVs is reduced by 18.25% to 94.93%, and the flight distance of the longest UAV flight is reduced by 15.97% to 37.33%. However, it can also be seen from the table that, compared to the running time of the task-adaptive clustering optimization algorithm, the GA-NSGA-II bilevel optimization algorithm takes a longer time, which is an obvious drawback of the GA-NSGA-II bilevelThe results suggest that the GA-NSGA-II algorithm offers a significant performance improvement in optimizing UAV task assignment and path planning. It effectively balances the workload of UAVs while significantly reducing the total flight distance, providing an efficient and balanced solution for UAV mission planning.