# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 5 Conclusions  
To address the challenges of task assignment and path planning in multi-UAV power grid inspections, we propose a GA-NSGA-II bilevel optimization algorithm based on task segmentation. By segmenting the total task into smaller unit tasks according to the UAV flight endurance, this approach avoids excessive search dimensions and solution failures. The proposed bilevel framework aims to balance inspection distances among UAVs while  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Number of** & **Total Flight** & **U1 Flight** & **U2 Flight** & **U3 Flight** & **U4 Flight** & **Distance** & **Sandard** & **Execution** \\  & **UAVs** & **Distance** & **Distance** & **Distance** & **Distance** & **Distance** & **Sandard** & **Time(s)** \\  & **(units)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **(m)** & **Deviation** & **Time(s)** \\ \hline GA-NSGA-II & 4 & 197,778 & 47,995 & 49,912 & 50,074 & 49,797 & 842.63 & 5874.43 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The result table for 406 task points.  
Figure 38: U4 flight sequence obtained by GA-NSGA-II algorithm in 406 task points.  
minimizing the overall inspection distance. In the outer loop, the NSGA-II algorithm optimizes task assignments across UAVs, while in the inner loop, an adaptive genetic algorithm with elitist retention optimizes the inspection sequence and direction within each UAV domain. They together yield a Pareto-optimal solution set, thereby satisfying all constraints and objectives. In scenarios featuring 284 tower inspection task points, our proposed algorithm has demonstrated significant improvements over the GA-GA bilevel optimization approach. Specifically, it has reduced the standard deviation of UAV flight distances by 41.91% to 84.63% and the longest individual flight distance by 29.41% to 43.98%. When compared to the task-adaptive clustering optimization method, the algorithm achieved a decrease in the standard deviation of flight distances ranging from 18.25% to 94.93% and a reduction in the maximum flight distance from 15.97% to 37.33%. Furthermore, the effectiveness of our algorithm was also validated in an environment with 406 tower task points. The results confirmed the superiority of the GA-NSGA-II bilevel optimization algorithm in minimizing the total inspection distance and achieving a more balanced distribution of workloads among UAVs. This highlights the algorithm's potential to enhance operational efficiency and fairness in UAV-assisted inspection tasks.  
Conceptualization, Y.L. and S.M.; methodology, Y.L. and C.C.; software, Y.L.; validation, Y.L. and Y.S.; formal analysis, Y.L., Y.S. and S.M.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L.; visualization, Y.L.; supervision, S.M. and Y.S.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.