## 5 Results
### Reliable Node Placing  
The third optimization problem was solved using data and results from the problem in the previous sections. The multiculture network design yielded by our method for each topology in Figure 6 are depicted in Figure 11. We note that a clustering effect is clearly observed in each of the designs. Furthermore, we note that, for most of the network topologies, the location of the different technologies ensures post-failure full connectivity for the remaining functioning nodes. In Kroenet and Reuna networks, however, maintaining full connectivity after a failure is not possible due to these topologies exhibit a low node degree. Remarkably, in cases like these two, the placing method properly assigns locations within the network according to the risk index of the technologies. In fact, the most vulnerable ones are allocated in places within the network that, after the occurrence of a failure, would compromise the network connectivity the least. This location mechanism is dictated by the formulation  
Figure 10: Number of nodes per technology parameterized by different selections of risk indexes. The values \(\alpha_{1},\alpha_{2},\alpha_{3}\) on the \(x\)-axis correspond to different selections for the risk indexes. Topology: Sprint network (\(n=11\) nodes). Figure 9: Number of purchased nodes per technology as a function of the maximum CAPEX available during the design process of the Sprint network (\(n=11\) nodes). of the optimization problem, which aims to maximize the number of available links after a failure, and minimize the number of connected components generated after failures. The values achieved by our proposed multiculture network design for the resilience metrics regarded here are listed in Table 1. In particular, the results are listed at the white background rows of the Table and, for comparison, in the gray background rows we list the results of the multiculture network design proposed in our previous work [13]. The results clearly show that the design methodology proposed here outperforms our earlier method. Despite Kreonet topology, the values achieved by the ATTR metric are, at least, equal to or larger than those reported previously. The better performance obtained by our new approach relies on the network-resilient node-placing mechanism, which after a failure maximizes the number of available links and minimizes the number of connected components. For example, consider the Reuna topology. From Figure 11h, after a failure, only when the least vulnerable technology fails the network gets disconnected. This can also be observed at column 4 of Table 1 labeled as \"Number of Post-Failure Connected Components \((t_{1},t_{2},t_{3})\).\"  
Figure 11: Optimal placing the network node devices for each technology for each network topology in Figure 6.  
However, since in our earlier work we placed nodes minimizing the number of links affected after a failure such type of solution was not feasible for the Reuna topology. For the Kreonet topology, we can comment that the configuration that maximizes Equation (10) was not obtained in the top 10% solutions for Equation (6). Such a result is explained by the random nature of the GA method, which does not ensure finding the optimal value. In Figure 12 we depict the relationship between the average node degree of each network topology and the values of the average number of post-failure connected components listed in Table 1. A pattern can be observed since Reuna and Kreonet, which present the lowest average node degree, also happen to be the multiculture networks with higher number of average post-failure connected components. This implies that both network connectivity and heterogeneous technologies are necessary to achieve