# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 4 Simulation Analysis
### Dynamic Simulation Results  
Based on the previous assumptions, five MEO satellites can each meet a capacity demand of 30 Gbps, and three GEO satellites can each meet a capacity demand of 100 Gbps. The total capacity provided by the satellites is 450 Gbps. Simulations were conducted over ten time intervals, each spaced one hour apart, to evaluate the performance of the proposed method under varying user demands. The choice of ten intervals was based on observed  
Figure 5: Comparison of PSO, GA, and SA algorithms.  
fluctuations in real-world satellite communication scenarios, where significant changes in demand patterns occur over a similar timeframe. Figure 6 presents the simulation results over ten intervals, with the horizontal axis representing the time, the left vertical axis representing the average user satisfaction, and the right vertical axis representing the total user demand, measured in Gbps. In the figure, the bar chart indicates the total user demand at each time point, while the line graph shows the average user satisfaction at each time point.  
From the figure, it can be seen that the average user satisfaction is greater than 0.996, fluctuating around nearly 100%, demonstrating that this method can effectively manage and plan the daily communication tasks of a large number of users dynamically. This indicates that the proposed method not only has significant advantages in handling high capacity demands but that it can also provide stable and efficient service quality in practical applications.  
To verify the effectiveness of the proposed dynamic task management method in distinguishing the communication needs of users with different priorities, we plotted five different average satisfaction curves for users with varying priority levels, where priority 5 is the highest and priority 1 is the lowest, as shown in Figure 7.  
The figure shows the user demand and satisfaction levels at ten different time points. The horizontal axis represents the time, the left vertical axis represents the average user satisfaction, and the right vertical axis represents the total user demand (in Gbps). From the figure, we can see that the total user demand at each time point, represented by the bar chart, fluctuates around 480 Gbps over time. The five lines represent the average satisfaction levels of users with different priorities, and several observations can be made.  
1. High-priority users (priority 5) consistently maintain an average satisfaction level above 0.95, with some instances approaching 1.0, indicating that the needs of high-priority users are prioritized at all times.
2. Medium-high-priority users (priorities 3 and 4) have average satisfaction levels fluctuating between 0.90 and 0.95, indicating that their needs are also well-met.
3. Medium-low-priority users (priorities 1 and 2) have relatively lower average satisfaction levels but still maintain satisfaction above 0.85, showing that even low-priority users' needs are met to a certain extent in most cases.  
Figure 6: Variation in average user satisfaction and total user demand over time.  
Additionally, from the total user demand and the satisfaction curves for each priority level at each time point, it is evident that the proposed method can dynamically adjust resource allocation in response to fluctuating demand. This ensures that high-priority users' needs are met first while also considering the needs of medium-low-priority users as much as possible. This demonstrates that the method can effectively handle a large number of users' communication tasks in practical applications. It can allocate resources based on different user priorities, thereby enhancing the overall service quality and user satisfaction of the system.