The increasing deployment of unmanned aerial vehicles (UAVs) in critical applications, including disaster management, surveillance, and smart cities, has highlighted the importance of efficient resource allocation. This chapter explores the optimization of UAV resource allocation, specifically focusing on UAV hovering, local computing, and task offloading in systems involving multiple UAVs. The proposed methodologies include the application of quantum-behaved particle swarm optimization (QPSO) and an advanced adaptive variant, AQPSO, to achieve optimal resource utilization and calculate energy cost. A case study demonstrates the effectiveness of these algorithms, showcasing improvements in energy consumption, task execution latency, and overall system performance. Simulation results validate the proposed strategies by comparing them to state-of-the-art approaches, paving the way for near-optimal solutions in UAV-enabled systems.