A Resource Scheduling Algorithm for Optical Ground Stations Supporting Satellite Observation Tasks

To address the resource scheduling problem of ground-based optical stations for key space target observation, this study proposes a station-structured genetic algorithm driven by dual-level tabu memory. The proposed method integrates station-grouped encoding, heuristic initialization, station-structured crossover, neighborhood-enhanced mutation, and local and global tabu-memory mechanisms to enhance the search capability under complex constraints and reduce redundant searches. Results from 30 independent runs show that, with population sizes of 150 and 300, the proposed algorithm achieves mean final fitness values of 15,162.784 and 15,870.237, respectively, corresponding to improvements of 62.54% and 63.84% over the single-point crossover strategy and 28.11% and 27.23% over the multi-point crossover strategy. Under the high-load condition with 250 candidate observation tasks, the total observation profit achieved by the proposed algorithm in the LEO target scenario is 30.09%, 9.14%, 13.78%, and 21.60% higher than that achieved by GA, TGA, ACO, and PSO, respectively. In the mixed MEO and HEO target scenario, the corresponding improvements are 29.94%, 6.26%, 13.22%, and 19.67%, respectively. The results demonstrate that the proposed method provides high-quality scheduling solutions, stable performance, and good adaptability to different orbital scenarios under complex resource constraints and high task loads.