## 4 Experiment Study
### Experiment Setup
#### 4.1.2 Competitors  
To verify the effectiveness of the algorithm proposed in this paper, we designed the following heuristic algorithms: Random, the earliest visual time window (VTW) start time first (WF), the profits first (PF), the profits/duration first (PDF), and the profits-window first (PWF) algorithm for comparisons. The details are as follows:  
1. Random: Randomly sort all to-be-scheduled tasks, insert the scheduling plan in order, and check whether the constraints are satisfied until the scheduling plan cannot accommodate any task. 2. WF: Sort all to-be-scheduled tasks from the earliest start time of the task's VTW to the latest start time of the task's VTW, insert the scheduling plan in order, and check whether it satisfies constraints until the scheduling plan cannot accommodate any task. 3. PF: Sort all to-be-scheduled tasks from high profit to low profit, insert the scheduling plan in order, and check whether the constraints are satisfied until the scheduling plan cannot accommodate any task. 4. PDF: Before sorting, divide the profits of all to-be-scheduled tasks by their respective observation duration time to obtain the unit observation time profits of the tasks. Sort these tasks from high unit observation time profit to low unit observation time profit, insert the scheduling plan in order, and check whether the constraints are satisfied until the scheduling plan cannot accommodate any task. 5. PWF: Sort all to-be-scheduled tasks from high profit to low profit, and when the tasks possess the same profit, sort them from the earliest start time of the task's VTW to the latest start time of the task's VTW. Insert the scheduling plan in order, and check whether the constraints are satisfied until the scheduling plan cannot accommodate any task. In addition, to verify the practicability of the heuristic construction neural network (HCNN) model as the initial solution provider of the meta-heuristic algorithm, we utilized two meta-heuristic representative iterative search algorithms, the tabu search (TS) algorithm [27; 28; 29; 30; 31; 32], simulated annealing (SA) algorithm [33; 34] and greedy randomized iterated search (GRILS) [25] for testing. SA and TS are classical algorithms for solving combinatorial optimization problems but are not designed particularly for solving AEOSSP. GRILS is a state-of-the-art algorithm for AEOSSP since it introduces the domain structure and INSERT operator according to the characteristics of the AEOSSP. The TS, SA and GRILS with the trained HCNN model as the initial solution are named TS-HCNN, SA-HCNN and GRILS-HCNN, respectively. Similarly, the initial solutions with other heuristic algorithms mentioned above are considered \"competitors\": the selected algorithms that are compared with the algorithm proposed in this study. Since the other three algorithms are not prevailing and classical, WF and PF are selected as the representative competitors. For example, the TS algorithm with the WF output was selected as the initial solution (TS-W), and the SA algorithm with the PF output was selected as the initial solution (SA-P). In TS and SA, new \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite** & \(a\) & \(e\) & \(i\) & _\(\omega\)_ & \(\Omega\)_ & \(m\) \\ \hline AS-01 & 7141701.7 km & 0.000627 & 98.5964 deg & 95.5069 deg & 342.307 deg & 125.2658 deg \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Satellite orbit parameters. solutions are both generated by the neighborhood operation, which reverses two random tasks in the current solution. Table 3 displays all competitors. There are 12 algorithms in the comparison algorithm experiment, including HCNN, WF, PF, GRILS, TS-HCNN, TS-W, TS-P, SA-HCNN, SA-W, SA-P, G-W, and G-P, and the maximum time is set as 10 min. In the TS algorithm, the neighborhood size is equal to the quadruple scale scene, and the local optimum is not allowed in 5 iterations; the initial temperature of SA is 4000\({}^{\circ}\), and the temperature decay rate is 0.999.