# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 4 Experimental Simulation
### Testing Performance
#### 4.3.2 Comparison of Algorithms Test  
Table 5 and Figure 7 present the performance of different optimization algorithms in terms of the task scheduled rate across 10 test scenarios for each instance size. The DRL application method is denoted as DRL. In the comparison of the DRL-LNS, GA [41], and ALNS [42] algorithms, the number of iteration times was set to 1500. Agile-satellite mission planning problem is NP-hard. Our goal was not to obtain the optimal solution, but rather to achieve a satisfactory feasible solution within the constrained resources and time, which differs significantly from ground-based optimization scenarios. We chose 1500 iterations as an economically viable solution, considering the trade-off between solution quality and computational cost. The specific value of 1500 iterations may vary based on the specific context. It was observed that the GA algorithm failed to find a feasible solution when the number of planning tasks exceeded 200. In contrast, the other comparison algorithms, including DRL-LNS and ALNS, successfully completed the planning process. As a result, the task scheduled rate of the GA algorithm exceeding 200 was 0. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Area**} & \multicolumn{4}{c}{**World**} \\ \cline{2-9}  & **50** & **100** & **150** & **200** & **100** & **200** & **300** & **400** \\ \hline train\_50 & 0.5080 & 0.3460 & 0.3387 & 0.3070 & 0.6160 & 0.5980 & 0.6020 & 0.6215 \\ train\_500 & 0.5120 & 0.3660 & 0.3493 & 0.3230 & 0.6560 & 0.6080 & 0.6120 & 0.6280 \\ train\_500 & 0.5360 & 0.3920 & 0.3827 & 0.3600 & 0.7020 & 0.6680 & 0.6667 & 0.6740 \\ trained & 0.5280 & 0.3860 & 0.3600 & 0.3310 & 0.6720 & 0.6280 & 0.6240 & 0.6470 \\ \hline \hline \end{tabular}  
* Metric is scheduled rate.  
\end{table} 
Table 4: Comparison of model generalization on train set. In Figure 7a, the task scheduled rate of our DRL-LNS algorithm is, on average, 11.35% higher than those of the ALNS for _Area_ instances. With the increase in instance size, the gap between ALNS and DRL-LNS increases and becomes the highest at 200, which is around 17%. It can be found that the task scheduled rate of DRL method is slightly lower (average 6.6%) than that of ALNS algorithm, which is consistent with the results obtained by the current main neural network learning algorithm [28; 30; 31]. From the above results, it can be found that the combination of the GAT-based model and heuristic algorithm brings about a notable enhancement in the computational efficiency of the algorithm. This improvement is particularly prominent when dealing with large-scale complex constraint problems that involve dense targets in local regions. At the same time, due to the increase in tasks that cannot be completed in the over-intensive scenarios, the scheduled rate declines with the increase in the number of _Area_ missions. Compared with the _Area_ scenarios, the _World_ instances involve planning more tasks, but the actual task distribution may be much sparser than the regional scope. As shown in Figure 7b, the DRL method achieved the best performance on the _World_ instances, in which the task scheduled rate of the DRL approach was, on average, 8.2% higher than those of the ALNS. In large-scale AEOS task scheduling problems, heuristic algorithms are limited by time and resource constraints and can only converge to suboptimal solutions. As the scale and complexity of the problem increase, along with tighter time and resource constraints, the quality of the solutions obtained through a heuristic search deteriorates, leaving room for improvement in solution quality for the DRL approach. Neural networks have an advantage in handling large-scale data. The use of the D3 RL model enhances the algorithm's adaptability to highly complex problems, and the GAT network allows for