## 5 Experimental Results and Analysis
### Experiment 2: POM-PSASIDEA Algorithm Resolves MSIMPLTS Scalability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Instance** & **S** & **T** & _Iefficiency_ & _Efficiency_ & _Rallocation_ & _Time_\({}_{res}\) \\ \hline
**LA\_100** & 8 & 100 & 703 & 696 & 99.0\% & 329.4 \\ \hline
**GA\_100** & 8 & 100 & 717 & 706 & 98.6\% & 346.5 \\ \hline
**LA\_150** & 8 & 150 & 1036 & 1021 & 98.6\% & 363.0 \\ \hline
**GA\_150** & 8 & 150 & 1051 & 1027 & 97.8\% & 387.7 \\ \hline
**LA\_200** & 8 & 200 & 1364 & 1336 & 98.0\% & 418.8 \\ \hline
**GA\_200** & 8 & 200 & 1417 & 1374 & 97.0\% & 430.0 \\ \hline
**LA\_250** & 8 & 250 & 1706 & 1665 & 97.6\% & 437.0 \\ \hline
**GA\_250** & 8 & 250 & 1775 & 1712 & 96.5\% & 453.8 \\ \hline
**LA\_300** & 8 & 300 & 2091 & 2028 & 97.0\% & 465.3 \\ \hline
**GA\_300** & 8 & 300 & 2121 & 2042 & 96.3\% & 475.8 \\ \hline
**LA\_350** & 8 & 350 & 2473 & 2386 & 96.5\% & 500.5 \\ \hline
**GA\_350** & 8 & 350 & 2491 & 2393 & 96.1\% & 533.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulated experimental results for the POM-PSAIDEA algorithm solved in different instances. Figure 10: _Cont._  
Figure 9: Performance analysis of the MSIPLTS-MLOO model for solving different instances of MSIPLTS in a large-scale target task scenario: (**a**) MSIPLTS-MLOO model when solving different instances of MSIPLTS; (**b**) mission benefits in the local region and the global region. Examples are given in Figure 10a-f for the task sizes of 100, 150, 200, 250, 300, and 350, respectively. The blue line segments indicate the convergence of task benefits in the global region, the black line segments indicate the same on a local scale, and the red circles indicate the difference-seeking points. As can be seen from Figure 10, instances with task sizes ranging from 100 to 350 in both the local and global regions show good convergence characteristics despite different task planning and simulation scenarios. In addition, the task convergence curves of each sub-figure show that the task benefit gradually converges to stability with the increase in the number of iterations of the algorithm, and the red circles in the figure illustrate the distribution of the differential search points. With the increase in the number of iterations, the search points are intensive in the early stage of the algorithm's evolution, while the convergence gradually decreases in the middle and late stages of the evolution, which reflects the POM-PSASIDEA algorithm at different stages of the optimization search adaptively adjusting the population size to balance the exploratory and exploitative nature to approach the local optimal solution faster and search for a high-quality task planning scheme. The simulation results of Experiment 2 show that the POM-PSASIDEA algorithm proposed in this work can effectively solve the MSIMPLTS-MLOO model in large-scale target task scenarios, and with an increase in the task size, it can solve the task planning scheme in a shorter time with a higher task benefit and task allocation rate. As such, the POM-PSASIDEA algorithm solves MSIMPLTS with stability. Figure 10: Benefit analysis of the POM-PSASIDEA algorithm for solving different instances of MSIMPLTS in a large-scale target task scenario. (**a**) Task benefit convergence for task number 100. (**b**) Task benefit convergence for task number 150. (**c**) Task benefit convergence for task number 200. (**d**) Task benefit convergence for task number 250. (**e**) Task benefit convergence for task number 300. (**f**) Task benefit convergence for task number 350.