# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 5 Experiment Results and Analysis  
To validate the effectiveness of the ITGA, this paper conducts simulation experiments on the algorithm. The algorithm is programmed in Python language, specifically version 3.10, and solved on a computer with an AMD Ryzen 7 processor running at 3.2 GHz, using 16 GB of RAM and the Windows 10 operating system. The algorithm parameters are set as follows: population size Pop = 50, maximum number of iteration Generations = 150, crossover probability = 0.9, mutation probability = 0.1, and tabu list length = 35. The satellite Two-Line Element (TLE) [20] data are sourced from SAIC's Space-Track, a global satellite and rocket data network. For the purpose of this experiment, a single remote sensing satellite ground station located at latitude 40.5\({}^{\circ}\), longitude 116.9\({}^{\circ}\), and an altitude of 0.057 km is selected. Furthermore, four experimental scenarios are designed for mission planning. In terms of satellite mission scheduling, this paper utilizes the STK [21] tool to calculate visible time windows and assigns a task to each visible window exceeding 6 min. Table 2 lists the number of tasks, total fitness, and the total mission duration for different experimental scenarios, identified by unique numbers with specific details concealed. Table 3 presents the ground station resources, including antennas, SDCs, demodulators, and RDs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Number of Tasks** & **Total Duration** & **Total Fitness** \\ \hline
1 & 41 & 21,077 & 46,159 \\
2 & 64 & 24,008 & 49,611 \\
3 & 67 & 20,987 & 32,202 \\
4 & 100 & 47,214 & 89,566 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental scene scale. To analyze the performance of the ITGA, we introduced several comparison algorithms, including a TGA without conflict resolution and task set partitioning methods, a DFS-integrated improved GA (DFSGA), a First-Come-First-Served Algorithm (FCFSA) [22], and a Priority Algorithm (PA). The DFSGA determines the chromosome individuals representing the task execution order through the genetic operations of the GA. It allocates ground station resources using DFS to calculate the fitness value of the chromosome individuals. The best individuals from the initial population are selected for the next iteration until the end of the iteration process. Finally, the optimal allocation scheme is determined by selecting the best individual from the final population. On the other hand, the FCFSA and the PA prioritize tasks based on execution time or task priority, respectively, for ground station resource allocation. We use average running time (ART), average fitness value (AFV), mean deviation (MD), average number of completed tasks (ANCT), average task execution rate (ATER), average non-ordinary task execution rate (ANOTER), and average convergence generation (ACG) as metrics to evaluate algorithm efficiency, convergence speed, optimization ability, and stability. However, due to the stochastic nature of the GA, the obtained results may not always be globally optimal solutions [23]. Therefore, in this study, we conducted simulation experiments by calculating the average of 10 runs for each experimental scenario. The simulation results are presented in Table 4. Notably, the \"0\" in the mean deviation metric indicates that the algorithm converged early in the running process. This occurred because the data transmission task set had fewer conflicts, and the algorithm found a globally optimal solution during the initial population generation. The \"-\" symbol indicates that the PA and the FCFSA only consider a single metric, leading to poor stability and rendering the average difference metric ineffective for them. The simulation results indicate that the ITGA performs well across various metrics in four different-sized experimental scenarios.