A Modified Seagull Optimization Algorithm with Latin Hypercube Sampling and Lévy Flight for 3D Path Planning of UAV

UAV path planning in complex urban environments faces significant challenges due to dense obstacles, narrow corridors, and stringent safety requirements. To address these issues, this paper proposes LLSOA, a modified Seagull Optimization Algorithm that integrates Latin Hypercube Sampling (LHS) for population initialization and Lévy Flight for global search. The key innovation lies in the problem-driven design: LHS ensures uniform coverage in dense urban maps, while Lévy Flight helps escape local optima. Compared with four state-of-the-art swarm intelligence algorithms (DBO, GWO, PIO, and PSO) across four urban scenarios, LLSOA achieves the best comprehensive fitness. Considering multiple constraints including path length, curvature, collision avoidance, and obstacle-avoidance logic, the trajectories generated by LLSOA show competitive overall performance, with no unsafe points recorded in the test scenarios and the best fitness values among the compared algorithms, albeit with a slight trade-off in path length. High-fidelity AirSim simulations with GPS/IMU noise further demonstrate that the planned trajectories remain within engineering acceptable limits. Compared with the noise-free baseline, the maximum trajectory deviation increases by 2.3% and the average deviation increases by 2.8% under high GPS/IMU noise. The main contributions are: (1) a problem-driven LLSOA that combines LHS and Lévy Flight, specifically tailored to dense urban environments; (2) theoretical analysis and simulation verification demonstrating its feasibility for multi-constraint path planning under the tested conditions; (3) high-fidelity (UE+AirSim) validation showing that the generated trajectories retain stability even under realistic sensor noise.