## 4 Results and Discussion
### Case Study for DMSA in Static Scheduling
#### 4.1.4 Simulation Results Analysis  
The simulation results of different cases are shown in Table 3. Generally speaking, with the increase in the number of satellites and the number of targets, the overall planning payoff is on the rise, but due to the increase in the complexity of the problem, the scheduling process also consumes more time.  
Comparison and Analysis for Performances of Optimization Algorithms  
From the perspective of search strategies among mentioned algorithms, CPSOA and CGA are both global search algorithms, while CTSA focuses on searching local solution space. From Figure 6, we can see that, when the problem scale is small, there is little difference between performances of different algorithms; due to CTSA's local search strategy, it has a considerable advantage in calculation speed over other centralized scheduling algorithms. With the increase of the problem scale, the superiority of the CPSOA's strong global search ability is reflected. In all experimental cases, CPSOA can output a scheduling plan with the highest payoff and completion rate, while CGA and CTSA fall much more easily into a local optimum.  
Comparison and Analysis for Scheduling Method  
From the perspective of scheduling methods, though the overall payoff will be lower than that of centralized scheduling, the distributed mission scheduling method has signif  
Figure 6: Payoff and scheduling time. Histogram shows the payoff of different algorithms’ optimization results, while the curve shows the scheduling time.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Case** & \multicolumn{2}{c}{**Case**} & \multicolumn{2}{c}{**DMSA**} & \multicolumn{2}{c}{**CGA**} & \multicolumn{2}{c}{**CPSOA**} & \multicolumn{2}{c}{**CTSA**} \\ \cline{2-13}
**No.** & **Sat** & **Tar** & **Profit** & **CR** & **RT(s)** & **Profit** & **CR** & **RT(s)** & **Profit** & **CR** & **RT(s)** & **Profit** & **CR** & **RT(s)** \\ \hline No.1 & 6 & 30 & 19 & 0.93 & 4.6 & 19.8 & 0.97 & 9.6 & 20.2 & 0.97 & 32.9 & 20 & 0.95 & 5.3 \\ No.2 & 6 & 50 & 31.2 & 0.96 & 4.8 & 29.3 & 0.90 & 21.5 & 31.6 & 0.96 & 38.7 & 30.4 & 0.94 & 7.8 \\ No.3 & 6 & 100 & 64.2 & 0.99 & 17.1 & 59.4 & 0.96 & 45.4 & 64.5 & 1 & 48.8 & 59.3 & 0.91 & 21.2 \\ No.4 & 8 & 100 & 60.7 & 0.98 & 19.9 & 57.9 & 0.92 & 56.2 & 61.6 & 0.98 & 78.6 & 57.2 & 0.86 & 20.8 \\ No.5 & 8 & 150 & 91.9 & 0.87 & 28.2 & 93.8 & 0.89 & 126.6 & 98.9 & 0.96 & 168.5 & 90.3 & 0.85 & 31.2 \\ No.6 & 16 & 150 & 93.4 & 0.91 & 28.8 & 95.3 & 0.93 & 272.6 & 100.3 & 1 & 370.7 & 90.1 & 0.84 & 43.7 \\ No.7 & 16 & 200 & 123.6 & 0.93 & 29.7 & 121.2 & 0.91 & 353.3 & 126.2 & 1 & 468.2 & 119.4 & 0.87 & 48.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation results in different cases.  
icant advantages in scheduling time. This is because the method of assigning targets to individuals for single-satellite scheduling is helpful in reducing the search areas of solution space, thereby improving the solving efficiency. Comparing the scheduling time between CPSOA and DMSA (as shown in Figure 7), we can see that when the scale of the problem increases, DMSA will not experience a sharp increase in scheduling time like CPSOA. Moreover, only the number of satellites increases while the number of targets remains unchanged, and the scheduling time of DMSA does not increase significantly, which fully demonstrates the advantages of the distributed system using decentralized management and control, that is, the addition or deletion of nodes will not affect the stability of the system. Individuals in the system can maintain contact and collaboration with each other through communication, while simultaneously remain relatively independent, which can greatly enhance the flexibility and robustness of the distributed system.