# Advancing Multi-UAV Inspection Dispatch Based on Bilevel Optimization and GA-NSGA-II
## 1 Introduction
combined mixed integer linear programming with reinforcement learning and adaptive large neighborhood search for UAV transmission line inspections, demonstrating superior optimization and stability, though the approach still faces challenges in computational complexity and real-time performance [27]. Finally, [PERSON] et al. used model predictive control in a mixed integer linear programming approach to handle task assignment and trajectory planning in obstacle-rich environments with connectivity constraints [28]. Heuristic algorithms have significant advantages over exact algorithms. First, heuristic algorithms can quickly solve problems in polynomial time, making them suitable for large-scale task planning. This avoids the issue of excessive solution time caused by the exponential growth of computational complexity in exact algorithms. For example, [PERSON] and [PERSON] et al. developed a FIFO-based genetic algorithm to optimize the scheduling of heterogeneous UAVs for package delivery. They did this because the mixed-integer programming model is difficult to solve within a reasonable time for large-scale instances, thereby verifying the reasonable solution time of heuristic algorithms compared to exact algorithms [29]. Second, heuristic algorithms have good adaptability and robustness. In power grid inspection, complex terrain and dynamically changing environments pose challenges for path planning. Heuristic algorithms can effectively deal with environmental changes and avoid local optima by introducing randomness, dynamic adjustment mechanisms, or combining with other algorithms. [PERSON] et al. proposeda model based on dynamic ant colony labor division to address the dynamic task assignment problem in UAV swarms. Through simulation experiments, they demonstrated the high self-organization, flexibility, and real-time responsiveness of heuristic algorithms in dynamic environments for task assignment [30]. Moreover, heuristic algorithms perform well in multi-objective optimization. Multi-UAV inspection tasks often involve multiple optimization objectives, such as minimizing the total flight distance, energy consumption, and mission completion time. Heuristic algorithms can flexibly balance these objectives to find compromise solutions. [PERSON] et al. developed a fuzzy multi-objective optimization method based on pigeon-inspired optimization (PIO) to solve the task assignment problem for UAVs targeting multiple objectives. The study established a multi-objective integer programming model with three objective functions and employed the PIO algorithm to address task assignment. This work validated the superiority of heuristic algorithms in multi-objective optimization [31]. Finally, heuristic algorithms are simple to implement and consume low resources, making them suitable for UAV systems with limited resources. For example, [PERSON] et al. proposed a learning-based heuristic algorithm to solve the charging station docking problem in UAV path planning. The algorithm demonstrates high efficiency in resource-constrained scenarios and can generate high-quality path planning solutions in a short time [32]. So, heuristic algorithms are capable of quickly generating high-quality solutions for multi-UAV power grid inspection task assignment and adapting to complex and dynamic environments while demonstrating good robustness and scalability. Compared to exact algorithms, they are an effective approach for solving task assignment problems within the framework of the MTSP. Given the complexity of large-scale power grid inspection tasks and the limitations of computational resources, this study adopts an offline path planning method. Although this method cannot respond to environmental changes in real time, it completes path planning before task execution, efficiently utilizing computational resources to ensure global optimality in large-scale scenarios. Moreover, this method is not limited to a specific type of UAV.