## 5 Simulation Experiments
### Coverage Analysis  
By setting the quantity of nodes in different regions to 50 and 100, respectively, Figure 11 displays the relationship between the quantity of holes and the coverage hole detection rate for the four algorithms in the target regions of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\). The analysis of Figure 11a,b shows that the hole detection rate of the different algorithms varies with the quantity of holes in the region. From Figure 11a, the hole detection ratio of the DHD-MEPO, C-CICHH, and 3D-VPCA algorithms are all 1 when the quantity of holes is up to 6. After the quantity of holes exceeds 6, the detection rates of the algorithms for the holes all decrease, but the DHD-MEPO algorithm decreases slowly compared to the other algorithms. At the quantity of holes of 12, it still has a detection rate of above 0.95 for holes. Similarly, in Figure 11b, the algorithm is in a dominant position for the detection of holes even in a network of \(100\times 100\times 100\) m\({}^{3}\). The detection rates of the C-CICHH and 3D-VPCA algorithms are consistently close to each other for holes, while the RA algorithm performs the worst. \begin{table}
\begin{tabular}{c c} \hline \hline  
Figure 10: Determination of the weighting parameters of the fitness function. The quantity of covered holes was set to 8 and 12 in different regions. Figure 12 displays the connection between the total quantity of repair nodes and the coverage rate of the four algorithms in the target regions of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\). The analysis of Figure 12a shows that in the \(10\times 10\times 10\) m\({}^{3}\) region, as the quantity of repair nodes increases, the location of the blind zones in the region is gradually covered. Using the algorithms in this paper to repair, the coverage rate grows faster than using the other three algorithms and is always in the leading position, and the coverage rate of DHD-MEPO and C-CICHH reaches 95% when the quantity of repair nodes is 12 above. The coverage rates of C-CICHH and 3D-VPCA are close to each other in the interval of 5-10 repair nodes, and the coverage rate of DHD-MEPO is close to 1 in the interval of 30 repair nodes. The reason for DHD-MEPO's leading coverage rate at different quantity of repair nodes is that the lens-imaging mapping learning strategy in the optimized emperor penguin algorithm is used to perturb the current optimal position of the repair node in the repair phase, which enhances the search capability of the algorithm in different directions and enables the node to find the appropriate optimal position to cover the hole faster. Figure 11: Plot of coverage hole detection ratio versus number of holes: (**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\({}^{3}\). Figure 12: Plot of quantity of repair nodes versus coverage: (**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 number of repair nodes is set to 12 and 18 in different regions. Figure 13 shows the relationship between the quantity of iterations and the hole repair ratio for the four algorithms in the target regions of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\). As the quantity of iterations of the algorithm run increases, some of the nodes in the region stop working due to energy depletion, so the quantity of holes as well as their location is constantly changing. The analysis of Figure 13 shows that after 200 rounds of work with all four algorithms, the hole repair rate essentially reaches over 90%. In Figure 13a, at 160 rounds of algorithm work, it can be observed that the C-CICHH hole repair ratio is higher than that of DHD-MEPO.