The design of routing algorithms for UAV swarm networks presents significant challenges due to their dynamic nature and constrained resources. Traditional routing approaches, such as flooding-based algorithms (e.g., OLSR, AODV), often incur substantial communication overhead, while greedy algorithms suffer from inefficiencies and the risk of converging to local optima. To address these limitations, this chapter explores intelligent UAV swarm routing algorithms. By leveraging replicator dynamics, evolutionary game theory facilitates adaptation to fluctuating network conditions, encouraging UAV nodes to share information and forward data packets.