Joint Position and Power Hierarchical Optimization for Multi-UAV Deployment Based on Density Peaks Clustering

With the rapid development of unmanned aerial vehicles (UAVs) technology, UAVs are increasingly deployed for communication, search and rescue operations. Nevertheless, determining optimal UAV positions and optimizing transmission power in complex environments remain significant challenges in current research. To address these issues, this paper presents a novel joint position-power hierarchical optimization (JPP-HO) algorithm. In particular, the approach initially employs Density Peaks Clustering (DPC) for pre-processing classification of ground users, with each cluster center designated as an initial UAV position. Subsequently, each UAV is constrained to operate within the designated cluster range, and the UAV positions are optimized using Particle Swarm Optimization (PSO) with a fixed transmit power. Finally, the transmit power is further refined through a Single-Objective Elite Genetic Algorithm (SEGA), forming an iterative optimization strategy that allows UAVs to adapt their positions and power dynamically for optimal communication in evolving environments. The simulation results demonstrate that the JPP-HO algorithm markedly enhances user coverage rates in comparison to the baseline methods, while concurrently optimising the utilisation of UAV power.