## 5 Experimental Results and Analysis
### Experiment 2: POM-PSASIDEA Algorithm Resolves MSIMPLTS Scalability  
To further verify the stability of the POM-PSASIDEA algorithm for solving MSIMPLTS, multiple sets of instances at different scales were used to verify the performance of the algorithm. For the local region of longitude [75; 135] and latitude [20; 50] and the global region of longitude [\(-180\), \(180\)] and latitude [\(-90\), \(90\)], instances with \(100\), \(150\), \(200\), \(250\), \(300\), and \(350\) target tasks were generated with a random distribution; a total of \(12\) groups of instances were generated for the simulation experiments. The experimental results of the POM-PSASIDEA algorithm for solving different task size instances of MSIMPLTS in large-scale target task scenarios are shown in Table 4. \"\(I_{Efficiency}\)\" denotes the task benefit in the ideal case, \"\(Efficiency\)\" denotes the task benefits in the actual case, \"\(R_{allocation}\)\" denotes the task assignment rate, and \"\(Time_{res}\)\" denotes the response time. The local area and global area simulation instances are represented by LA_Number and GA_Number, respectively, where Number is the number of target tasks. Note that, since the target tasks were randomly generated, the ideal task benefits are different in each instance. Figure 9 shows the performance analysis of the MSIMPLTS-MLOO model for different instances of large-scale target mission scenarios, specifically six sets of instances with the number of target missions of \(100\), \(150\), \(200\), \(250\), \(300\), and \(350\) under the local region and the global region. Figure 9a shows the mission benefits in the local and global regions. With the increase in the number of satellites and targets, the mission benefits in the local region are always higher than those in the global region, which is because, in the local region, since the target tasks are relatively concentrated, the model can schedule the satellite resources more efficiently and prioritize the demand for high-priority tasks, thus achieving higher mission benefits. Figure 9b illustrates the mission response time and mission allocation rate. With the increase in the number of satellites and targets, the mission response time increases slightly in the instances within the local region and global region, but the increase is more moderate. In addition, the task allocation rate has not decreased significantly in each group of instances and has always been maintained at a high level, which indicates that the MSIMPLTS-MLOO model has high efficiency and stability in resource allocation and can ensure that most of the tasks can be allocated to satellites for execution in a timely and effective manner. Figure 9 shows that, as the number of satellites and targets increases, the MSIMPLTS-MLOO model is still able to achieve a fast response to the mission planning scenarios when dealing with MSIMPLTS and performs excellently in terms of mission benefits and mission allocation rates. Figure 8: Schematic of the distribution of the final MSIMPLTS-MLOO mission planning scheme. In addition, Figure 10 illustrates the convergence of the POM-PSAIDEA algorithm in terms of task benefits for different task sizes (100 to 350).