## 5 Conclusions  
This paper studied agile Earth observation satellite (AEOS) scheduling problems with temporal dependencies. The current scheduling methods make it difficult to solve large-scale AEOS scheduling problems efficiently. Thus, we have proposed a knowledge-guided parallel hybrid local search algorithm to enhance the search efficiency of AEOS scheduling.  
We improved the algorithm's global search and optimization capabilities by leveraging prior satellite knowledge to enhance new solution generation in local searches and incorporating deferred mechanisms and a tabu strategy into hill climbing algorithms. Additionally, using FP-growth-based data mining methods enables the algorithm to obtain high-quality solutions quickly. The testing results have revealed that the proposed KG-PHLS algorithm outperforms the other algorithms in terms of profit and computation time on various scenario instances, particularly for large-scale problems.  
More complex tasks could be completed via complex and heterogeneous satellite networks, which include various types of satellites. Our future research directions could include:  
* Expanding from single-satellite scheduling to multi-satellite scheduling necessitates considering additional constraints and optimization objectives such as load balancing and integrating satellite data transmission into scheduling to unify both aspects for joint scheduling.
* We need to address more uncertainties, such as the cloud cover, urgent task arrival, and their impact on agile satellite scheduling.  
Conceptualization, Y.S. and S.C.; methodology, Y.S. and S.C.; coding, Y.S.; validation, Y.S. and X.W.; investigation, Y.S.; writing--original draft preparation, Y.S. and S.C.; writing--review and editing, X.W., S.C., M.Z. and L.X.; visualization, Y.S. and S.C.; supervision, S.C.; project administration, L.X.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant no. 61806119), the Natural Science Basic Research Plan in Shaanxi Province of China (no. 2024 JC-YBMS-516), Fundamental Research Funds for the Central Universities (no. GK202201014).  
The code will be open source after the paper is accepted.  
Thanks to the anonymous reviewers and editor from the journal.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number of Tasks**} & \multicolumn{2}{c}{**Parameter Value**} & \multirow{2}{*}{**Profit**} & \multirow{2}{*}{**Computation Times (s)**} \\  & \(k_{n}\) & \(U_{L}\) & & \\ \hline \multirow{6}{*}{300} & 0.1 & 0.05 & 1114.80 & 63.51 \\  & 0.05 & 0.05 & 1089.00 & **62.69** \\  & 0.2 & 0.05 & **1149.80** & 85.78 \\  & 0.1 & 0.02 & 1109.20 & 65.51 \\  & 0.1 & 0.1 & 1118.60 & 65.20 \\ \hline \multirow{6}{*}{400} & 0.1 & 0.05 & 1279.60 & 106.26 \\  & 0.05 & 0.05 & 1252.20 & **98.58** \\ \cline{1-1}  & 0.2 & 0.05 & **1292.00** & 130.61 \\ \cline{1-1}  & 0.1 & 0.02 & 1271.80 & 114.12 \\ \cline{1-1}  & 0.1 & 0.1 & 1273.00 & 109.88 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Sensitivity analysis of the design decisions.