###### Abstract  
With the increasing number of satellites and rising user demands, the volume of satellite data transmissions is growing significantly. Existing scheduling systems suffer from unequal resource allocation and low transmission efficiency. Therefore, effectively addressing the large-scale multi-objective satellite data transmission scheduling problem (SDTSP) within a limited timeframe is crucial. Typically, swarm intelligence algorithms are used to address the SDTSP. While these methods perform well in simple task scenarios, they tend to become stuck in local optima when dealing with complex situations, failing to meet mission requirements. In this context, we propose an improved method based on the minimum angle particle swarm optimization (MAPSO) algorithm. The MAPSO algorithm is encoded as a discrete optimizer to solve discrete scheduling problems. The calculation equation of the sine function is improved according to the problem's characteristics to deal with complex multi-objective problems. This algorithm employs a minimum angle strategy to select local and global optimal particles, enhancing solution efficiency and avoiding local optima. Additionally, the objective space and solution space exhibit symmetry, where the search within the solution space continuously improves the distribution of fitness values in the objective space. The evaluation of the objective space can guide the search within the solution space. This method can solve multi-objective SDTSPs, meeting the demands of complex scenarios, which our method significantly improves compared to the seven algorithms. Experimental results demonstrate that this algorithm effectively improves the allocation efficiency of satellite and ground station resources and shortens the transmission time of satellite data transmission tasks. p +
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[name=A, description=AData transmission scheduling is an NP-hard problem [2]. Also, adding more ground stations cannot address the rapidly growing number of satellites. Research on the conflict between limited ground station resources and the growing number of satellites has been ongoing for over a decade. In the 1990s, [PERSON] [3] used a mixed-integer programming model to address the scheduling problem of the satellite control network (AFSCN) in the United States. Later, [PERSON] [4] employed heuristic algorithms for the same issue and summarized the evolution of the AFSCN problem. Since then, many scholars have applied various evolutionary algorithms to address these problems [5; 6]. For complex SDTSPs, swarm intelligence algorithms have become a primary solution method [7; 8; 9]. In recent years, new technologies have emerged [10] to solve satellite data transmission issues. Although early studies have achieved some success in exploring SDTSP, they have not fully considered the cyclical nature of resource competition, the unique aspects of large-scale data transmission missions (SDTT), and the multi-dimensional optimization objectives. For instance, Earth observation satellites operate in fixed orbits, and ground stations have relatively fixed antenna receiving areas. When satellites enter these areas, they periodically compete with ground stations for data transmission, especially during large-scale transmissions. Traditional heuristic algorithms, such as simulated annealing or genetic algorithms, often fail to handle large-scale data transmission problems due to their complexity and time-consuming global search processes. Furthermore, current scheduling algorithms frequently fall into the trap of locally optimal solutions, failing to use the available time window resources fully. To tackle these challenges effectively, employing suitable algorithms for managing large-scale multi-objective SDTSPs [11] is essential. Multi-objective evolutionary algorithms (MOEAs) have been widely used due to their excellent performance in practical problems [12; 13].