# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 5 Experiment Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Case** & **Algorithm** & **MD** & **ANCT** & **ART** & **AFV** & **ATER** & **ANOTER** \\ \hline \multirow{4}{*}{1} & DFSGA & 0 & 38 & 19.71 & 44,029 & 92.68\% & 94.12\% \\  & TGA & 0 & 38 & 12.85 & 44,029 & 92.68\% & 94.12\% \\  & ITGA & 0 & 38 & 12.35 & 44,029 & 92.68\% & 94.12\% \\  & FCFSA & - & 34.7 & 4.36 (ms) & 39,007.7 & 84.63\% & 84.71\% \\  & PA & - & 34.5 & 4.51 (ms) & 37,738.3 & 84.15\% & 83.53\% \\ \hline \multirow{4}{*}{2} & DFSGA & 183.4 & 45 & 24.35 & 36,636 & 70.31\% & 66.09\% \\  & TGA & 131.2 & 45 & 14.36 & 37,399.4 & 70.31\% & 77.27\% \\  & ITGA & 125.8 & 46 & 12.95 & 37,720 & 71.88\% & 77.27\% \\  & FCFSA & - & 36.6 & 6.12 (ms) & 30,683.9 & 57.19\% & 64.55\% \\  & PA & - & 37.8 & 6.05 (ms) & 29,539.4 & 59.06\% & 63.86\% \\ \hline \multirow{4}{*}{3} & DFSGA & 0 & 54 & 28.75 & 31,768 & 96.43\% & 100.00\% \\  & TGA & 0 & 54 & 16.71 & 31,768 & 96.43\% & 100.00\% \\  & ITGA & 0 & 54 & 15.02 & 31,768 & 96.43\% & 100.00\% \\  & FCFSA & - & 50.3 & 7.17 (ms) & 29,712.2 & 89.82\% & 94.39\% \\  & PA & - & 50.6 & 7.79 (ms) & 29,604.8 & 90.36\% & 91.46\% \\ \hline \multirow{4}{*}{4} & DFSGA & 127.5 & 93.7 & 52.24 & 85,544.9 & 93.40\% & 97.14\% \\  & TGA & 55.3 & 95 & 25.19 & 86,750 & 95.00\% & 98.39\% \\ \cline{1-1}  & ITGA & 9.5 & 95 & 23.78 & 86,750 & 95.00\% & 98.39\% \\ \cline{1-1}  & FCFSA & - & 77.2 & 13.11 (ms) & 73,876.3 & 77.20\% & 86.94\% \\ \cline{1-1}  & PA & - & 75.2 & 14.42 (ms) & 68,601.2 & 75.20\% & 76.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the results of the algorithms. In the scheduling results, there were 17 data transmission tasks that could not be allocated ground station hardware resources and were therefore scheduled not to be executed. Among these, 10 were important or urgent tasks that could not be allocated resources due to their data transmission durations being shorter than those of other critical tasks. One task could not be allocated ground station equipment due to a link conflict and was therefore abandoned. The remaining six tasks were deemed ordinary priority and could not be accommodated through trimming their transmission durations, leading to their abandonment. The distribution of antenna usage time is shown in Figure 10.  
The black squares represent moments when a particular antenna is being utilized for a satellite data transmission task, with the width of the square indicating the duration of antenna occupancy. Due to our consideration of load balancing during equipment allocation, the antenna usage time distribution is relatively uniform. Within any conflicting time period, there is no situation where satellite data transmission tasks are concentrated on a single antenna for execution. If an antenna is already occupied by a transmission task, other transmission tasks will be assigned to idle antennas. During non-conflicting periods, load balancing is not considered due to the absence of equipment quantity bottlenecks. Additionally, since other equipment exhibits similar effects, further elaboration is not necessary. Regarding the ACG metric, the FCFSA and PA algorithms based on greedy strategies do not involve an iterative process. Therefore, in the comparison of ACG metrics, these two algorithms are not considered. The comparison of ACG metrics for the remaining three algorithms is presented in Table 5.  
Figure 10: ITGA antenna usage time distribution diagram. Figure 9: Ground resource allocation results solved by the ITGA in case 2. As shown in the ACG metric comparison chart, the GA enhanced with DFS partially addressed the issue of slow convergence due to its weak local search capability. However, its convergence speed is still slow compared to the TGA and the ITGA. Both the TGA and the ITGA achieve convergence within 60 generations, with the ITGA exhibiting a 54.56% reduction in average convergence generations.