## 2 Related Work  
At present, with the rapid development of UAV technology, the application scenarios of UAVs are becoming more and more extensive. Nowadays, research on UAVs includes UAV coverage, the communication between UAVs and multi-UAV collaboration. However, the energy consumption problem of UAVs limits their applications. This issue has attracted more and more attention, and clustering technology plays a crucial role in reducing the energy consumption of AUSCNs. At the same time, with the continuous development and optimization of heuristic algorithms, more and more people are applying heuristic algorithms to the optimization research of multi-UAV collaboration [26]. Therefore, this chapter mainly discusses two aspects: UAV application research and sensor network clustering optimization. First, in terms of the application of UAVs, in [27], [PERSON] et al. proposed an integrated model based on intelligence and mechanism applications, which were optimized by using the sand cat swarm optimization algorithm and the reverse ant colony algorithm to achieve the best planting routes. In order to select appropriate cluster heads in the UAV swarm of the cooperative communication network, in [28], [PERSON] et al. proposed a UAV clustering method based on improved cluster head selection weights to improve UAV clustering and data collection efficiency. However, that method cannot find suitable cluster heads when facing large-scale drones. In [29], [PERSON], [PERSON] et al. proposed a drone swarm deployment algorithm based on MOEA/D (DSDA-MOEA/D) to balance the energy consumption of all drones and maximize the lifetime of the full coverage network. However, this method cannot find the optimal coverage solution. As people pay more and more attention to network security, in [30], [PERSON] et al. proposed three different meta-heuristic algorithms: the cuckoo search algorithm (CUCO), the differential evolution algorithm (DEA) and the bee algorithm. The HBA is used to optimize FANET to improve throughput as well as reduce PDR and delay in order to improve communication quality and reliability. However, due to fixed algorithm parameters, this method can easily fall into local optima and fail to obtain the optimal solution. In [31], [PERSON] et al. proposed a strawberry optimization algorithm (SBA) to solve the problem of unmanned placement and reduce the number of unmanned vehicles under full coverage. However, this method can easily fall into evolutionary stagnation. It is impossible to obtain a coverage solution that meets specific scenarios. Then, in terms of the network clustering optimization of wireless sensors, in [32], a multi-sink deployment strategy, based on an improved particle swarm clustering optimization algorithm, was proposed for clustering optimization between nodes, which can effectively increase the network life and reduces energy consumption, but that method cannot find the optimal clustering scheme while maintaining low energy consumption. In [33], a new hierarchical adaptive energy-efficient clustering routing (HAECR) method is proposed for underwater data collection, which divides hierarchical regions according to the depth of sensor nodes, which can effectively reduce energy consumption and extend the network life, but the algorithm converges too slowly and does not take into account complex underwater biological factors. In [34], the authors proposed a new algorithm based on social group optimization (SGO) to reduce communication distances to reduce network energy consumption. In addition, the use of new optimization techniques to dynamically select the head node can significantly reduce the energy consumption of the network. However, due to the complexity of the calculation, the convergence speed of the algorithm cannot reach an acceptable level.