In order to enhance the odor source localization capability of unmanned aerial vehicles (UAVs) in three-dimensional environments, a crowding factor-elitist strategy improved sparrow search algorithm (CE-SSA) is proposed. This algorithm incorporates elite strategy, crowding factor, and L-vy flight perturbation mechanisms to improve search capability and effectively avoid local optima. In the experiments, the diffusion of the odor plume in a 3D space was simulated, and the performance of CE-SSA was compared with that of the classical particle swarm optimization (PSO) and the original sparrow search algorithm (SSA). The results show that, in the case of a single UAV, CE-SSA reduces localization error by over 98% compared to traditional algorithms and increases the success rate by more than 56%. When the number of UAVs reaches four or more, the localization error stabilizes below 0.2 meters, with a success rate of 100%. Moreover, CE-SSA demonstrates strong robustness under different odor plume characteristics and can handle complex environmental variations. The study indicates that CE-SSA offers significant advantages in improving localization accuracy and success rate, providing a reliable solution for UAVbased odor source tracking in complex environments. The findings of this research provide theoretical support for the further development of active olfactory technology and expand the potential applications of UAVs in environmental monitoring and disaster response.