The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.
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