# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 1 Introduction
This hybrid approach has proven to be more efficient than existing techniques [11]. Furthermore, considering that resolving satellite data transmission conflicts can provide heuristic information for future satellite revisits, scholars have proposed an improved genetic algorithm that combines immune algorithms. This combined approach is capable of addressing scenarios that prioritize both the efficiency and stability of satellite data transmission scheduling [12]. Collectively, these methodologies highlight the high efficiency of the GA and its potential for integration with other algorithms to address their inherent limitations, further enhancing their performance [13]. However, considering multiple hardware resource constraints will further increase the difficulty of solving the ground station hardware resource scheduling problem. If the satellite data transmission scale also increases synchronously, the solution efficiency of the genetic algorithm will be affected to a certain extent. Some scholars combined the depth-first search (DFS) algorithm and the GA, and fully considered the constraints of the actual ground station receiving system to solve the ground station resource scheduling problem with a single station, multiple antennas, and multiple tasks [14]. Considering the constraints of antennas, inverters, demodulators, and recording devices, DFS is used to improve the local search ability of the genetic algorithm and improve the quality of the solution. Heuristic information is used to optimize the search process of DFS, so that the method can calculate the optimal solution faster. However, as the scale of satellite data transmission gradually increases, DFS is more and more time-consuming, which reduces the solution efficiency of the method. Therefore, hybrid algorithms based on the genetic algorithm dealing with large-scale ground station hardware resource scheduling problems need to be further studied. In summary, we propose an ITGA combined with heuristic rules. Firstly, the data transmission task set is divided into non-conflicting sub-task sets using a divide-andconquer approach. Secondly, initial chromosomes representing the execution order of the data transmission tasks are randomly generated, and the allocation of ground station hardware resources is determined based on this order to calculate the fitness values of these chromosomes. In the ground station's hardware resource scheduling, the algorithm combines a conflict resolution method based on heuristic rules with the ground station's resource allocation method. Data transmission tasks that cannot be executed due to conflicts are pruned in order to enhance both the overall quality of the initial population and individual chromosome quality during iterations. By combining the tabu search algorithm (TSA) with the GA, improvements are made to traditional mutation and crossover operators, preventing repeated searches and avoiding local optimal solutions while considering constraints related to ground station resources. Consequently, an optimal solution for allocating hardware resources in satellite data transmission ground stations is obtained, which has been verified through experiments demonstrating its effectiveness and reliability. The main contributions of this paper are as follows: A constraint satisfaction model considering multiple ground resources is proposed as a solution model for the hardware resource scheduling problem in satellite data transmission ground stations. An ITGA combined with heuristic rules is introduced to improve the solution efficiency and stability for the aforementioned problem. The superiority of the ITGA is verified through comparative simulation experiments. The remaining parts of the paper are organized as follows: Section 2 introduces types of ground resources and the receiving system of the ground station and analyzes the satellite data transmission process and transmission conflicts.