## 5 Experimental Results and Analysis
### Experiment 1: POM-PSASIDEA Algorithm Resolves MSIMPLTS Effectiveness  
To verify the effectiveness of the POM-PSASIDEA algorithm in processing MSIMPLTS, this experiment generated 100 target tasks uniformly in a local range of longitude [75; 135] and latitude [20; 50], which was used to simulate the task requirements in a large-scale target scenario. The geographic distribution of the 100 target tasks is shown in Figure 6.  
Firstly, the POM-PSASIDEA algorithm was used to solve the MSIMPLTS model under 100 target scenarios, and the allocation optimization stage matched several satellite and target task allocation sequences to form an initial task planning scheme based on the satellite resources and target task information. The initial planning results are shown in Table 2, where \"T\" denotes the target mission number, \"Sat\" denotes the satellite number, and E denotes the mission benefit. Based on the initial mission planning scheme shown in Table 2, the distribution relationship between the executing satellites and the target missions was visualized as shown in Figure 7. This figure represents the grouped bars of the initial mission planning scheme, with each group of sub-bars corresponding to a satellite (Satellite 1 to Satellite 8), where the x-axis represents the target mission number and the y-axis represents the target mission benefit. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No. Sat**} & \(a\) & \(e\) & \(i\) & \(RAAN\) & \(\omega\) & \(\varphi\) & \(\theta_{j}\) & \(t\mathbf{max}_{S_{j}}\) & \(A_{j}\) & \(w_{j}\) & \(r_{j}^{s}\) \\  & \(/(\mathbf{km})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) & \(/(^{\circ})\) \\ \hline Sat1 & 6871 & 0.013 & 98.54 & 179 & 94.764 & 109.153 & 6 & 400 & 30 & 0.2 & 2 \\ Sat2 & 6871 & 0.021 & 97.42 & 159 & 97.034 & 122.516 & 6 & 400 & 30 & 0.2 & 2 \\ Sat3 & 6871 & 0.016 & 90.32 & 139 & 90.972 & 162.317 & 6 & 400 & 30 & 0.2 & 2 \\ Sat4 & 6871 & 0.014 & 98.50 & 119 & 92.612 & 119.562 & 6 & 450 & 30 & 0.3 & 2 \\ Sat5 & 6871 & 0.037 & 97.90 & 99 & 92.860 & 157.373 & 7 & 450 & 35 & 0.3 & 2 \\ Sat6 & 6871 & 0.028 & 96.31 & 79 & 92.172 & 188.254 & 7 & 450 & 35 & 0.3 & 1.5 \\ Sat7 & 6871 & 0.055 & 97.44 & 59 & 97.629 & 172.768 & 8 & 500 & 35 & 0.3 & 1.5 \\ Sat8 & 6871 & 0.051 & 90.65 & 29 & 93.795 & 103.553 & 8 & 500 & 35 & 0.4 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite orbit parameters and satellite payload parameters. Figure 6: Geographic distribution of the 100 target tasks. Combining Table 2 and Figure 7, it can be seen that all 100 target tasks are effectively assigned in the assignment optimization phase, and the POM-PSASIDEA algorithm can effectively solve the MSIMPLTS-MLOO superstructure model to generate the initial mission planning scheme containing the satellite and target task assignment sequences. After that, it enters the time window optimization phase to allocate appropriate time windows for each target mission based on the initial mission planning scheme. Table 3 shows the time window allocation scheme in the time window optimization phase, where \"T\" denotes the target mission number, \"St\" denotes the time window observation start time, and \"Et\" denotes the time window observation end time. Table 3 shows that all 100 target tasks are assigned to visible time windows, and the time window optimization phase can assign a visible time window to each target task for observation.