An Improved Knowledge-Based Genetic Algorithm for High-Priority Task Scheduling in Relay Satellite Networks

This paper presents an improved knowledge-based genetic algorithm for relay satellite task scheduling. It aims to address the issue of ensuring high-priority tasks under complex constraints and resource competition. The algorithm enhances the high-priority task completion ratio by incorporating multiple knowledge sorting strategies and a dynamic crossover mechanism during population initialization. Simulation results show that the proposed algorithm improves the high-priority task completion ratio by an average of 11.78 percentage points compared to the second-best algorithm. In environments with high load, resource constraints, and capacity shrinkage, the proposed algorithm outperforms in scheduling efficiency, robustness, and adaptability. It demonstrates effectiveness in multi-task and multi-resource competitive environments. IKBGA provides a highly targeted and scalable optimization solution for relay satellite task scheduling, with strong application potential.