# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 1 Introduction  
As the scale of power grids continues to expand, the maintenance of transmission systems is becoming increasingly difficult, especially in harsh environments and complex terrain conditions, where traditional inspection methods are inefficient and cannot achieve full coverage, affecting the quality of power grid inspections. Owing to its characteristics of rapid response, high flexibility, high safety, and strong imaging capabilities, the unmanned aerial vehicle (UAV) can effectively cover areas that are hard to reach by manual inspections, can significantly improve the efficiency and accuracy of power grid inspections, and has promising potential in application. Currently, the UAV inspection approach has become the mainstream method, but its overall efficiency is still limited by the quality of task assignment and path planning. Therefore, the study on UAV-based power grid inspection, especially for its task assignment and path planning, has great significance to improve the power grid inspection efficiency, energy security, and economic development [1, 2, 3, 4, 5, 6]. Compared to manual inspections, UAV-based inspections offer advantages such as lower costs and reduced security risks [7]. However, single UAV inspections often facelimitations, such as a restricted range and higher failure risks. Multi-UAV cooperative inspection technology mitigates these risks by expanding the inspection coverage, enhancing efficiency through redundancy, and offering flexibility in adjusting inspection strategies [8; 9; 10; 11], which makes it more valuable in practical applications. Despite these benefits, the implementation of multi-UAV collaborative inspections still encounters several challenges: Balanced task assignment: To optimize inspection efficiency, tasks must be allocated evenly among UAVs to prevent overloading any single unit [12; 13]. Optimal path planning: it is crucial to plan the most efficient inspection route for each UAV to minimize energy consumption and enhance inspection efficiency, all while ensuring task completion [14; 15]. Expansion of inspection scope: extending the inspection strategy to cover large-scale power grids is necessary to improve system adaptability and coverage [16; 17]. Task assignment in multi-UAV power grid inspections is a typical multiple traveling salesman problem (MISP) with constrains [18]. In the literature, various methods have been proposed, including exact algorithms like linear programming, heuristic approaches such as simulated annealing, neural network-based methods like self-organizing neural networks, and a hybrid combination of these approaches. Heuristic algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and clustering methods, are particularly effective for large-scale task assignment and path planning, as they can quickly approximate optimal solutions through heuristic rules [19; 20]. [PERSON] et al. improved bidirectional ant colony and honey badger algorithms for task assignment and path planning in challenging environments, but their algorithms are computationally intensive and unsuitable for large-scale scenarios [21]. [PERSON] et al. developed a coordination optimization algorithm using k-means clustering and genetic algorithms to reduce flight distances, but it struggles with balanced task assignment due to its reliance on geographical clustering [22]. [PERSON] et al. proposed a simulated annealing algorithm for small-scale scenarios, but its efficiency declines in large-scale applications [23]. Optimization methods such as dynamic programming, depth-first and breadth-first search, and mixed integer linear programming have also been explored [24]. [PERSON] et al. introduced a dynamic ant colony division model to track heterogeneous UAVs' positions in real time, showing strong self-organization and adaptability to dynamic environments [25]. [PERSON] et al. proposed a discrete particle swarm optimization algorithm for dynamic task assignment in changing environments [26]. [PERSON] et al.