# 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  
1. Task-adaptive clustering optimization algorithm: The task-adaptive clustering optimization algorithm firstly distributes the tasks of the cut grid line units by clustering the algorithm and then optimizes the order and direction of the UAV inspection unit tasks using the adaptive genetic algorithm based on the elite retention strategy. Although the algorithm can quickly generate inspection schemes, it is difficult to balance the tasks assigned by cluster, resulting in a large difference in inspection distances between UAVs; the flight routes are shown in Figures 22-26. Figure 21: U5 flight sequence obtained by GA-NSGA-II algorithm in 284 task points. Figure 23: U2 flight sequence obtained by adaptive clustering algorithm in 284 task points. Figure 24: U3 flight sequence obtained by adaptive clustering algorithm in 284 task points. In Figure 22, UAV U1 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 10, finally landing at hive f1. In Figure 23, UAV U2 takes off from hive f3, 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 6, finally landing at hive f1. In Figure 24, UAV U3 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 f1. In Figure 25, UAV U4 takes off from hive f4, first inspects the towers on the route marked as 1, then returns to f5 for battery swapping, and continues the inspection in the order of 1 to 6, finally landing at hive f4. In Figure 26, UAV U5 takes off from hive f5, 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 2, finally landing at hive f5. The flight routes of the UAVs clearly illustrate a significant imbalance in mission execution. UAV U1 needs to take off and land from the hive 10 times to complete the inspection of the assigned power poles, whereas UAV U5 only requires two takeoffs and  
Figure 25: U4 flight sequence obtained by adaptive clustering algorithm in 284 task points. Figure 26: U5 flight sequence obtained by adaptive clustering algorithm in 284 task points. landings. This substantial disparity in flight distances leads to a considerable difference in the time each UAV spends on its mission. Consequently, the overall efficiency of the inspection process is severely compromised, and the potential advantages of multi-UAV inspections are not fully realized. 2. GA-GA bilevel optimization algorithm: The GA-GA algorithm aims to minimize the total inspection distance of UAVs. The GA algorithm is used to optimize task assignment in the outer layer, and the adaptive genetic algorithm based on the elite retention strategy is used to optimize the inspection order and direction of UAVs in the inner layer. However, the algorithm only focuses on single-objective optimization and fails to achieve the balanced distribution of the inspection distance among the UAVs, resulting in a large difference in flight distance between UAVs; the flight routes are shown in Figures 27-  
Figure 31: U5 flight sequence obtained by GA-GA algorithm in 284 task points. Figure 30: U4 flight sequence obtained by GA-GA algorithm in 284 task points. Figure 29: U3 flight sequence obtained by GA-GA algorithm in 284 task points. In Figure 27, UAV U1 takes off from five f4, 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 5, finally landing at hive f5. In Figure 28, UAV U2 takes off from hive f3, 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 11, finally landing at hive f5.