Continuous Hopfield Neural Network (CHNN)-Based Clustering Optimization Scheme for FANETs

With the rapid development of UAV technology, Flying Ad hoc Network (FANET) has become a core network architecture in military, disaster relief, and other fields due to its potential for efficient communication and collaboration in dynamic environments. However, existing clustering algorithms exhibit significant drawbacks in dynamic adaptability, parameter adjustment complexity, and network robustness. To address these challenges, this paper proposes a clustering optimization algorithm CHNN-CA based on a continuous Hopfield neural network (CHNN). The goal is to enhance the stability and efficiency of FANETs through multi-objective dynamic balance optimization. A CHNN optimization model that incorporates node mobility prediction and energy sensing is constructed to cope with the network’s time-varying characteristics. Additionally, a dynamic penalty function is designed to strengthen cluster size and connectivity constraints, ensuring the convergence of the algorithm under topology uncertainty. Simulation results demonstrate that CHNN-CA achieves a 23.6% improvement in cluster stability and an 18.9% reduction in control overhead compared to the traditional weighted clustering algorithm (WCA) under highly dynamic scenarios. The algorithm also simplifies parameter tuning through a lightweight adaptive function and combines link quality and multi-hop coverage mechanisms to effectively address the problem of cluster head over-density in large-scale networks. This provides an innovative solution for efficient management of dynamic FANETs.