## 5 Simulation Results  
For the floating nuclear power plant, the normal operation state is: G1, G2, G3 and G4 directly supply power to the load through their respective main switchboard, the tie lines are all out of operation and the emergency power sources are in the hot standby state. That is, all the solid line parts in Figure 2 are put into operation, the dotted line parts are out of operation and all loads are put into operation with the rated power. The per-unit power value of each load and generator in the system is given in Table 1 along with the load class. The per-unit power value of each tie line is 1 p.u.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Per-Unit**} & \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Per-Unit**} & \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Per-Unit**} \\  & \multicolumn{1}{c}{**Power/Load Class**} & & & **Power/Load Class** & & & **Power/Load Class** \\ \hline L\({}_{1}\) & 1.0/3 & L\({}_{11}\) & 0.0375/2 & L\({}_{21}\) & 0.0375/2 & L\({}_{31}\) & 0.15/1 \\ L\({}_{2}\) & 7.0/3 & L\({  
When network reconstruction is needed to recover the power supply of the whole network, for MPGA, the population number is set as 10, the number of individuals in each population is set as 20 and the optimal population remaining algebra is set as 100. The above parameters are set considering the balance between calculation time and reliable convergence to the optimal solution. The generation gap value represents the proportion of individuals with low fitness being screened out in the genetic iteration process. When the generation gap value is high, the algorithm has more iterations and is even difficult to converge. However, a low generation gap value will lead to premature convergence. Multiple simulation results show that when the value is set as 0.9, the algorithm can ensure both rapidity and reliability. For the genetic algorithm, if the crossover probability between populations is too large, the mutation probability will be too small, the individuals in the population will be too concentrated and it will be easy to fall into local convergence. However, if the crossover probability is too small and the mutation probability is too large, it will be difficult to find the optimal individual in the evolution process. In this paper, the crossover probability range is set as 0.7\(\sim\)0.9, and the mutation probability range is set as 0.01\(\sim\)0.03. The crossover probability and mutation probability of each population randomly occur within the above range. To accelerate the convergence speed of the algorithm, referring to the updating direction principle of optimal particle guidance in a particle swarm algorithm, after individual crossover and mutation in the population, the crossover link between each population and the optimal population is added, and the optimal crossover probability ranges from 0.01 to 0.1.  
When calculating individual fitness, in Equation (1), \(a_{1}=6\), \(a_{2}=3\), \(a_{3}=2\); in Equation (2), \(k_{\text{p}}=30\) and \(k_{\text{m}}=1\); in Equation (4), \(w_{1}=0.85\), \(w_{2}=0.1\) and \(w_{3}=0.05\). Based on the network topology of the floating nuclear power plant shown in Figure 1, MATLAB software is used to conduct the simulation of the proposed method. The PC used for the following simulation case results adopts Intel I7-8700 with a 3.2 GHz processor and 8.00 GB memory.