# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 1 Introduction  
In recent years, technological advancements in aerospace engineering have led to a rapid increase in the number of remote sensing satellites. This expansion has resulted in a substantial surge in data transmission tasks and the associated volume of data [1], straining the existing ground station infrastructure beyond its capabilities. Despite the surge in satellite data, the hardware resources of most ground stations have not undergone commensurate upgrades, thus exposing the shortcomings of traditional scheduling methods in managing burgeoning resource allocation demands. These conventional approaches are increasingly inadequate, frequently failing to efficiently allocate finite hardware resources, which gives rise to significant bottlenecks and impedes timely data reception. Consequently, there is an imperative need to develop and implement innovative strategies that enhance data reception processes considering the existing hardware resource limitations [2]. Tackling this challenge is pivotal to improving the overall efficacy and dependability of satellite data transmission systems. The scheduling of ground station hardware resources is an NP-hard problem [3]. Approaches to its solution include heuristic algorithms, intelligent search algorithms, and hybrid algorithms. Some scholars, utilizing a vast array of effective information derived from accumulated antenna scheduling data from current remote sensing satellite ground stations, have proposed an intelligent scheduling algorithm that synergizes Long Short-Term Memory neural networks with heuristic rules [4]. Others have introduced animproved Ant Colony Optimization algorithm with dynamically adjustable parameters, specifically targeting the task and resource conflicts arising from complex satellite data transmission scenarios [5]. Moreover, researchers have considered decomposing the multi-ground station resource scheduling problem into several individual ground station resource scheduling issues, incorporating constraints related to antennas and recording equipment, and have suggested an enhanced algorithm that combines heuristic methods with Lagrange relaxation techniques [6]. While the methods investigated by these scholars can, to a certain extent, address the problems of ground station resource scheduling, they predominantly consider only antennas or recording equipment. In practice, however, not only do these components come into play, but hardware resources such as frequency converters and demodulators, along with their corresponding constraints, are also involved. If the constraints tied to ground station resources are fully taken into account, the aforementioned methods are unable to yield the optimal solution to the ground station hardware resource scheduling conundrum. The genetic algorithm (GA) has the ability to customize coding modes, fitness functions, individual selection mechanisms, crossover strategies, and mutation strategies according to problem characteristics [7], and has excellent global search capability [8], which can effectively deal with the resource scheduling problem of ground stations [9; 10]. In the current research on the GA for ground station resource scheduling, limited attention has been paid to incorporating diverse hardware resource constraints. Instead, studies focusing on single-type hardware resource constraints predominate, providing valuable insights. Recognizing the increasing demand for dynamic resource rescheduling in ground stations and the limitations of single-objective optimization, scholars have proposed multi-objective dynamic rescheduling algorithms based on the GA. These algorithms have demonstrated superior performance in terms of optimization and computational overhead compared to existing methods [2]. To address the requirements of theme-oriented satellite data transmission and ensure the timeliness and integrity of thematic data, researchers have introduced an enhanced genetic algorithm integrated with particle swarm optimization.