An Enhanced MOPSO Algorithm for Multi-Objective UAV Path Planning in Mountainous Environments

Path planning for unmanned aerial vehicles (UAVs) in mountainous environments requires satisfying terrain clearance and obstacle avoidance constraints while optimizing path length, flight time, and energy consumption. To address these challenges, this paper proposes EC-MOPSO (Epsilon-dominance and Crowding-distance-based Multi-Objective Particle Swarm Optimization). Inspired by the principle of symmetry, the algorithm integrates an adaptive parameter adjustment mechanism with a ε− dominance–crowding archiving strategy to balance global exploration and local exploitation through spatially symmetric archive management. A safety-repairable B-spline trajectory model ensures smooth and feasible flight paths under complex terrain conditions. Simulation results show that EC-MOPSO reduces path length by 10–40%, improves normalized hypervolume by over 25%, and decreases performance variance by 20–25%, confirming faster convergence and higher robustness compared with representative multi-objective optimization approaches. Ablation studies further verify that both the adaptive parameter mechanism and the ε− dominance–crowding strategy significantly enhance convergence stability and overall optimization performance. Overall, EC-MOPSO provides an adaptive and reliable optimization framework for generating efficient, safe, and energy-aware UAV trajectories in real-world mountainous rescue missions.