###### Abstract  
Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively. Network energy consumption; heuristic algorithm; network clustering; UAV monitoring; wireless sensor network; cluster head selection  
31 August 202330 September 202319  
With the continuous development of wireless sensor technology, intelligent unmanned AUSCNs bring many significant benefits in the agricultural field [9; 10]. However, a key issue facing AUSCNs is how to effectively reduce their energy consumption [11; 12]. To solve this problem, network clustering has become a feasible method. However, with the advancement of hardware technology, the number of sensors in AUSCNs has increased rapidly, resulting in the complexity of the network topology [13; 14; 15]. It is difficult for traditional clustering algorithms to find appropriate head nodes to reduce energy consumption [16]. Furthermore, this problem is proven to be NP-hard. Therefore, it is of great significance to propose a new AUSCN clustering evolutionary algorithm. This algorithm is expected to effectively cope with the challenges of increasing the number of sensors and reducing energy consumption, promoting the wider application of AUSCNs in the agricultural field [1; 17]. In recent years, with the combination of UAVs with agriculture and many other scenarios, more and more people are optimizing the deployment and monitoring of UAVs and the communication between UAVs. In [18], [PERSON] et al. proposed a multi-objective particle swarm optimization (MOPSO) for agricultural UAV monitoring deployment. In [19], [PERSON] et al. proposed incremental gray wolf optimization (I-GWO) and extended gray wolf optimization (Ex-GWO) for the path planning of agricultural robots to complete various tasks in farmland. In [20], in order to solve problems such as UAV flight time periods, [PERSON] et al. proposed a WOA-OLSR algorithm to optimize the flight self-organizing network to provide energy-saving and safe routing solutions. In [21], [PERSON] et al. proposed a hybrid genetic algorithm for the optimization of UAV networks to solve the deployment problem of UAVs and thereby improve UAV group coverage. However, the above methods converge slowly and can easily fall into local optimal solutions. When faced with complex network parameters, they cannot find the optimal AUSCN clustering scheme.