## 2 Related Work
### Hybrid Strategy  
Considering the slow response time and temporal limitations of a global approach, as well as the high signaling overhead and that the local approach lacks a global view and is prone to local optima and cascading congestion scenarios, [PERSON] and [PERSON] et al. [11] proposed a hybrid global-local load balancing (HGL) routing algorithm for connecting the IoT through satellite networks. This scheme optimizes routing decisions by combining global and local load balancing mechanisms. At the global level, a global load balancing strategy is established by collecting the load information of various devices in the IoT, and the optimal global path is selected in the satellite network. At the local level, local load balancing and path optimization are achieved by considering direct communication between devices and the assistance of neighboring devices. Computing capability is a valuable resource in satellite internet, particularly for satellites serving as space nodes. The size, weight, and power constraints of satellites limit their computing capabilities, posing greater challenges for routing algorithms. To meet the requirements of satellite storage and processing capabilities, [PERSON] and [PERSON] et al. [12] proposed a routing table generation and updating algorithm that delegates the routing calculations of the satellite network to onboard and ground routers. It separately generates local network routing tables and global network routing tables. This algorithm can reduce the demands on satellite computing power, alleviate the burden on inter-satellite links, and allow for upgrades to ground routers as the satellite network expands. Furthermore, [PERSON] and [PERSON] et al. [13] proposed an approach that combines precomputation with distributed onboard real-time computation. Taking into account real-time inter-satellite link states, this method calculates the next-hop routes and forwarding tables on each satellite, enhancing real-time performance and reducing the computational load on satellites. [PERSON] et al. [14] proposed an energy-sensitive and congestion-balanced (ESCB) routing scheme. To address the limited energy resources of satellites, they introduced a tubular sliding time window (TSTW) model. This model enables the prediction of remaining energy and employs a multi-objective algorithm to calculate routing paths. [PERSON] et al. [15] proposed a state-aware routing algorithm based on traffic prediction to disperse network traffic. They designed a spatial-temporal attention fusion graph neural network (STAFGNN) to predict future network states by capturing the spatial-temporal correlation of satellite network traffic. Additionally, they designed multipath routing to reduce the probability of link congestion by dispersing all end-to-end traffic.  
From Table 1, it is evident that most algorithms consider latency as an evaluation metric while mitigating network congestion, highlighting the importance of latency as a crucial factor. However, there is a notable absence of algorithms that consider path stability as a factor. Due to the high dynamism inherent in satellite networks, path switching is unavoidable due to satellite movements. Therefore, the selection of more stable paths to reduce the cost of path switching is a worthwhile issue for exploration. Consequently, this paper proposes a novel congestion mitigation method that takes into account both latency and path stability as evaluation metrics. Leveraging a multi-objective simulated annealing algorithm, the approach aims to enhance path stability while maintaining a minimal latency cost.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Strategy** & **Algorithm** & **Optimization** & **Latency** & \begin{tabular}{c} **Computing and** \\ **Energy Cost** \\ \end{tabular} & **Throughput** & \
\begin{tabular}{c} **Packet** \\ **Loss Rate** \\ \end{tabular} &
\begin{tabular}{c} **Path** \\ **