Energy-efficient altitude optimization in multi-UAV search and rescue: A hybrid swarm approach

The Internet of Things (IoT) has revolutionized disaster response by enabling real-time data acquisition, processing, and communication through edge devices that significantly improve the efficiency of Urban Search and Rescue (USAR) operations. This work presents a novel hybrid optimization approach by integrating Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to solve the NP-hard problem of minimizing the number of UAVs required for efficient area coverage. The performance of the proposed algorithm is evaluated by providing a comparison with GA-based, PSO-based, and fixed-altitude approaches. UAV altitude, energy capacity, and coverage radius are considered as key optimization parameters. Four navigation techniques including Uniform Grid Omni Navigation, Uniform Vesica Omni Navigation, Boundary Intersect Grid Omni Navigation, and Boundary Intersect Vesica Omni Navigation are used to reduce redundant waypoints and improve energy efficiency. In addition, a comprehensive energy model is considered that links UAV altitude to coverage area and waypoint distribution, providing a critical trade-off between coverage area and energy consumption. Simulation results is validated through case studies in NUST and Masdar City which show that the hybrid grid-based approach is highly effective for both regular and irregular area coverage, offering improved efficiency and minimizing UAV deployment. The proposed approach outperforms other methods, providing an efficient sub-optimal solution for real-world USAR UAV operations.