Topology-Aware Genetic Clustering with Elite Opposition-Based Learning for High-Mobility FANETs

To address the frequent cluster structure reconfiguration caused by the high mobility and dynamic topological changes in Flying Ad Hoc Networks (FANETs), this paper proposes an optimal Cluster Head (CH) selection mechanism based on an adaptive genetic clustering algorithm (AGCA). Traditional clustering methods often struggle with multi-objective conflicts and fixed-length encoding limitations in highly dynamic environments. The proposed AGCA dynamically characterizes the cluster topology using a variable-length genetic encoding scheme to adaptively respond to network scale variations. Furthermore, a comprehensive multi-objective fitness function is constructed to jointly optimize energy balance, communication distance, and topological stability. To enhance cluster quality and accelerate convergence, an Elite Opposition-Based Learning (EOL) strategy and a chaotic initialization method are integrated into the algorithm. Extensive simulation experiments demonstrate that, compared with the classical Weighted Clustering Algorithm (WCA), the proposed AGCA achieves superior performance in highly dynamic FANET environments. Specifically, the IGCA extends the average cluster head lifetime by 22%, reduces end-to-end delay by 23%, increases network throughput by 18%, and decreases control overhead by 30%, thereby verifying its scientific validity and robustness.