The purpose of this study is to solve the problem of shortening the endurance time of UAV clusters in rainfall environment by analyzing the attenuation characteristics of laser transmission and optimizing the energy distribution strategy. The focus is on quantifying the influence of rainfall intensity on laser energy transfer efficiency and UAV energy consumption, and developing a parameter adaptive charging strategy to extend the overall stagnation time. Therefore, the hovering energy consumption model of UAV is established, and the influence of raindrop kinetic energy on the energy consumption of UAV is included. The validity of the model is verified based on the experimental data of Inspire 2 four-rotor UAV. At the same time, the power budget model of laser wireless power transfer link is constructed based on Beer-Lambert law to characterize the attenuation law under light rain, moderate rain and heavy rain (5~25 mm/h). The model integrates the attenuation of laser transmission in rain, beam divergence, photoelectric conversion efficiency, communication energy consumption and subsystem loss, and more accurately calculates the received power of each UAV. An improved Spider Wasp Optimizer (SWO) algorithm is proposed. By introducing the Adaptive Moment Estimation optimizer (ADMA), the crossover probability of the SWO algorithm is dynamically adjusted through the gradient adaptive mechanism to improve the adaptability of the SWO algorithm to complex and dynamic environments. Enhance the overall robustness, adapt to different optimization stages, find a better balance between exploration and development, guide the update direction through gradient information, and improve the local search effect. On this basis, a meta-heuristic algorithm is proposed to calculate the influence of rainfall weather on laser transmission power in real time, which enhances the accuracy of the charging strategy. Combined with the working priority, charging efficiency and spatial position of the UAV, the reasonable allocation of energy is realized, the overall stagnation time is improved, and the landing rate is effectively reduced. Simulation experiments on 3-10 UAVs show that the proposed Adaptive Spider Wasp Optimizer Charging (AD-SWOC) strategy has significant advantages over FCFS and NJNP. Under 5 mm/h rainfall, AD-SWOC achieved a maximum increase of 31.34% in overall hover time, which was better than that of FCFS (22.1%) and NJNP (25.7%). In 25 mm/h rainfall, although the laser attenuation is serious, the average receiving power is reduced to 110 W, and the overall stagnation time can still be increased by 20.09%. The adaptive crossover probability mechanism makes the forced landing rate significantly lower than the fixed parameter method. When the UAVs reach 10, only 4 UAVs are forced to land, and the number of forced landings with fixed crossover probability is more than half of that of UAVs. The AD-SWOC strategy effectively alleviates the problem of UAV swarm endurance by systematically integrating rainfall-related laser attenuation analysis, adaptive optimization and multi-factor priority determination. The established model can accurately predict the laser power attenuation and UAV energy dynamics under different rainfall conditions, and support efficient energy management. By optimizing the charging scheduling based on real-time environment and task constraints, the strategy significantly prolongs the task duration and reduces the interruption caused by forced landing. This study provides a new theoretical and technical reference for the improvement of the stagnation ability of UAV swarms in other complex media. In the future, artificial intelligence technology can be introduced to predict more accurately according to meteorological conditions such as rainfall, air pressure and wind speed.