Solar energy is a ubiquitous and free-of-charge source of renewable energy. When combined with other sources and applied to drones, this source of energy can significantly enhance drone endurance. In this paper, a novel approach is used to model photovoltaic solar cells under various environmental conditions. Photovoltaic (PV) cells are modeled and simulated using double and three-diode models with the ant colony optimization (ACO) algorithm to study the behavior of PV cells at different temperatures. This research uses a CdTe thin-film cell whose experimental data are extracted from the SCAPS simulation program at temperatures of 250 – 310 K and irradiance of 1000 W/m2. In 30 runs and 200 iteration steps with a population of 30 ants, the best root-mean-square error (RMSE) value is 5.2936 × 10-5 and 1.711 × 10-5 in double and three-diode cases, respectively. This shows that efficiency and performance of photovoltaic solar cell embedded in drones has improved drastically compared to other similar researches, which indicates the considerable superiority of the proposed method over many other algorithms developed so far. In each iteration step, the average time is 2.304 and 2.612 s in double and three-diode models, respectively. The number of investigated points is between 83 – 93 points depending on the temperature of the experiment in descending order. Moreover, the corresponding optimization has been performed on a 30-core server with 32GB RAM. Keywords: Ant Colony Optimization (ACO) Algorithm, Photovoltaic Solar Cell, Drone, Endurance, Power Efficiency.