## 5 Simulation Experiments
### Energy Consumption Analysis  
Each of the four algorithms is subjected to several independent experiments under the same conditions to obtain the graph in Figure 14 between the quantity of working rounds and the total energy consumed in a target area of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\). The analysis of Figure 14a shows that the nodes' overall energy consumption using the RA algorithm is more than that consumed by the other three algorithms, and the nodes' overall energy consumption reaches 152.3 J after 200 rounds of operation. The nodes' overall energy consumption using the DHD-MEPO and C-CICHH and 3D-VPCA methods is approximately equal before the algorithm has been run 40 times. Because the main source of the sensor nodes' energy consumption in the current network is receiving and sending data information, and the repair nodes have not all started to work yet, the energy consumed by moving the distance to repair the hole is not obvious. The analysis of Figure 14a,b shows that after 200 rounds of work using the DHD-MEPO algorithm, the total node consumption is 128.4 J and 1584 J, respectively, which is a significant saving of energy for the nodes compared to the energy consumed using other algorithms, improving  
Figure 13: Graph of the quantity of iterations in relation to the hole repair ratio: (**a**) Relationship map within the area \(10\times 10\times 10\) m\({}^{3}\); (**b**) Relationship map within the area \(100\times 100\times 100\) m\({}^{3}\).  
the energy efficiency and extending the lifecycle of hybrid WSNs. The reason for this is that by dividing the network into cells and selecting groups of nodes to manage other nodes within the cell and aggregate data information during the hole detection process, the coordination avoids the unnecessary energy consumed by all nodes to send information to the center of the BS. The analysis of the 120 rounds of algorithm work in Figure 14b displays that DHD-MEPO is slightly higher than C-CICHH and 3D-VPCA, mainly because in the process of repairing holes, DHD-MEPO will give priority to the hole regions with a high importance level, even though there are hole regions near the current repair node that are a shorter distance to repair. However, the level degree of these hole regions is not high, so it will move a longer distance to repair the region with a higher importance level within the longest distance to which this node can move according to the multi-objective fitness value, so the energy consumed will be high.  
The relationship between the DHD-MEPO and C-CICHH, 3D-VPCA algorithms in terms of each mobile node ID and energy consumed in a target region of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\) is analyzed. From Figure 15a,b, it can be seen that 3D-VPCA performs the worst, C-CICHH slightly outperforms 3D-VPCA, and DHD-MEPO significantly outperforms 3D-VPCA and C-CICHH when considering the overall energy consumption and the uniformity of the mobile node energy consumption. Compared with C-CICHH and 3D-VPCA, a single node's maximum energy consumption and the nodes' uniformity of the overall energy consumption are effectively reduced by the inclusion of the energy balance coefficient of the repair nodes as a measure in the multi-objective function setting process, so that all repair nodes will maintain a certain level of energy balance to repair the holes. In contrast, 3D-VPCA and C-CICHH minimize the nodes' overall energy consumption to some extent, but do not allow for the optimization of the uniformity of energy consumption of the mobile nodes, which leads to the performance differences shown in Figure 15.