## 4 Results and Discussion
### Analysis of Repair Strategies  
#### 4.3.1 Resilience Assessment Under Multiple Strategies  
The optimization process is illustrated in Figure 7. It shows the optimization process of the network recovery strategy as the number of iterations increases. In the landslide disaster scenario (Figure 6(a)), the genetic algorithm converged to the optimal solution after the 12 th iteration, demonstrating good convergence and suitability for the metro network topology model constructed in this paper. The maximum network resilience value was 0.628, with the corresponding optimal recovery node sequence being Gaofeng Lake, Huanshan Park, Daping, Gailan Creek, Fuhua Road, Ciqikou, Yuegang North Road, Huxia Street, Zhongliang Mountain, and Sanban Creek. In the flood disaster scenario (Figure 6(b)), the genetic algorithm converged to the optimal solution after the 10 th iteration, with a maximum network resilience value of 0.646. The corresponding optimal recovery node sequence was Bijin, Baosheng Lake, Liujiaping, Xingke Avenue, Luqi, Shuangbei, Shuangfeng Bridge, Lushan, Xiangjiagang, and Jiandingpo.  
Table 6 lists the node recovery order and their network resilience values under each recovery strategy.  
Figure 6: Distributions of high-risk disaster stations.  
Figure 7: Optimal recovery curve of network resilience after node failures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Disturbance Scenario** & **Recovery Strategy** & **Node Recovery Order** & **Resilience Value** \\ \hline \multirow{7}{*}{Landslide disaster} & \multirow{4}{*}{Random Recovery} & \multirow{4}{*}{Park–Cidjou–Cidjou Lake–Huasia Street–Fuhua} & \multirow{4}{*}{0.440} \\  & & & Read–Zhongling Mountain–Doping \\  & & & Dapping–Fuhua Road–C  
In the landslide disaster scenario, the optimal recovery strategy using the genetic algorithm performs exceptionally well. Specifically, the network resilience values under random repair, importance, service efficiency index, and the optimal recovery strategy from the genetic algorithm are 0.440, 0.441, 0.570, and 0.628, respectively. The optimal recovery strategy from the genetic algorithm improves resilience by approximately 42.9%, 42.4%, and 10.3% compared to random repair, topological importance, and service efficiency index, respectively. This optimal recovery strategy not only significantly enhances network resilience but also better ensures the stability and reliability of the network during node failures. By comprehensively considering the overall topological performance of the network and the service efficiency of nodes, it achieves the ultimate optimization of resilience.  
In the flood disaster scenario, the network resilience values under random repair, topological importance, service efficiency index, and the optimal recovery strategy from the genetic algorithm are 0.470, 0.474, 0.617, and 0.646, respectively. The optimal recovery strategy from the genetic algorithm improves resilience by 37.5% compared to random repair, by 36.2% compared to the strategy based on topological importance, and by 4.6% compared to the strategy based on service efficiency index. These results further validate the significant enhancement effect of the genetic algorithm's optimal strategy on network resilience in the context of flood disasters, providing the best recovery plan for the network in the case of multiple node failures.