Hybrid metaheuristic-driven 3D path planning for UAVs in complex urban environments: a multi-objective fusion framework - Qing Cheng; Zhengyuan Zhang; Yunfei Du; Xi Zhao (Geocarto International)
Unmanned Aerial Vehicles (UAVs) offer transformative potential for urban logistics, but their deployment is challenged by complex 3D path planning in constrained environments. This study introduces GS-PSO, a novel multi-stage hybrid framework designed to address this challenge by sequentially integrating Particle Swarm Optimization (PSO), Sparrow Search Algorithm (SSA), and a Genetic Algorithm (GA). Key innovations include dynamic chaotic inertia weights in PSO for enhanced exploration and specialized GA operators for fine-grained refinement. The efficacy of GS-PSO was validated on the CEC2020 benchmarks, where it found the global optimum in 8/10 functions, showing superior accuracy and stability over standard PSO. In high-fidelity UAV simulations, GS-PSO generated paths up to 22.9% shorter than the AVOA, significantly outperforming competitors. Statistical tests (p < 0.05) confirmed its superior solution quality and robustness. This work provides an effective, reliable solution for complex UAV trajectory planning.