Dynamic Routing Optimization Method for UAV Swarm Networks: An Evolutionary Game Approach

With the ongoing advancement of information and communication technologies, the communication technologies for UAV swarm networks have undergone rapid development, especially in the context of large-scale UAV network deployments. In recent years, UAVs have found wide-ranging applications in both military and civilian domains. However, the inherent complexity and high dynamic nature of UAV swarm activities present substantial challenges to traditional routing algorithms, prompting the need for the design and implementation of efficient and sustainable routing solutions. To address these challenges, this paper introduces a UAV swarm routing algorithm based on evolutionary game theory, with a particular focus on energy efficiency and resource optimization. We leverage evolutionary game theory to enhance cooperation among nodes and adopt a strategy update rule that imitates the best-performing agents. In the proposed algorithm, nodes engage in continuous packet forwarding and participate in game interactions with neighboring nodes, adjusting their strategies based on accumulated gains. This strategy not only significantly enhances network lifetime and improves the packet delivery rate but also optimizes energy consumption and resource utilization, aligning with sustainable computing principles. To validate the effectiveness of the proposed routing method, we conduct extensive simulation experiments within a designed and implemented system model under different environmental contexts. The analysis confirmed the accuracy and effectiveness of the proposed routing method, highlighting its exceptional performance in terms of the network survival time, the number of successfully transmitted packets, and the adaptability in dynamic scenarios.