To address the challenge of low-altitude path planning for clusters of unmanned aerial vehicles (UAVs) in military electromagnetic denial environments, a novel approach using ultraviolet light is proposed. This method takes advantage of ultraviolet light's low background noise and all-weather wide field of view, enabling non-line-of-sight communication and maintaining links between UAV clusters by combining a hemispherical multi-input multi-output structure. An improved moderate random particle swarm optimization (MRPSO) algorithm that incorporates dynamic weights is proposed, alongside strategies like antiroulette wheel selection, chaotic distribution factors, and the Metropolis criterion, to enhance global search capabilities and optimize path planning. The simulation results demonstrate that MRPSO outperforms traditional PSO, hybrid inertial traction PSO, and spherical PSO by increasing the success rate of path breakthroughs in radar-free environments by 7.65%, 7.68%, and 29.71%, respectively. In complex radar environments, improvements are more evident, with increases of 18.19%, 14.86%, and 43.99%, respectively. When the algorithm converges, it exhibits low fitness values and notable advantages in convergence rate and optimization stability, demonstrating its effectiveness and versatility across different application scenarios. This study offers a notable contribution for enhancing low-altitude breakthrough path planning of UAVs in electromagnetic denial environments.