## 5 Conclusions and Future Work  
This document provides an overview of the routing algorithms used in the SDN network controller, including their quantitative performance characteristics, such as the number of hops, available bandwidth, delay and packet loss, and their throughput, considering a large-scale multi-domain network topology. This work suggests that the proposal of a genetic algorithm in the routing process can significantly improve network performance. Minimizing the cost of network metrics and reducing the likelihood of selecting connections with few resources or poor link performance can significantly reduce data flow performance or, in the worst case scenario, cause congestion. The experiments in this work show that our proposal based on the MOCell algorithm provides optimal routing by having a global view of the network and abstracting its QoS metrics. The proposed MOCell method adapts to any routing constraint, while the standard algorithms in the controller do not consider any of these essential properties to improve the routing service.  
Managing a centralized environment as in SDN can significantly enhance the routing process by improving path selection and providing better service. Evolutionary algorithms and their variants take advantage of this problem because they are scalable and can find reasonable solutions during the first run, which helps networks to meet QoS requirements and improves network performance.  
In future work, we will consider optimizing the algorithm for different population sizes, as well as different crossover and mutation methods. Similarly, we will evaluate the performance of the MOCell algorithm in more detail compared with other evolutionary algorithms such as NSGA-II and MOEA/D, considering convergence time and diversity of solutions. In the evaluation of other evolutionary algorithms, both their convergence to the Pareto front and the dispersion of the set of found solutions are evaluated, while in this paper we focus exclusively on the selection of a path from the solutions found by the methods. Therefore, we consider it appropriate to use other metrics such as cardinality, accuracy, and diversity to evaluate the performance of evolutionary multi-objective algorithms.  
Conceptualization, J.E.G.-T., R.R.-R., A.T. and J.E.L.-R.; methodology, J.E.G.-T., A.T. and R.R.-R.; software, J.E.L.-R.; validation, R.R.-R. and A.G.-M.; formal analysis, A.T.; investigation, J.E.L.-R., A.T., R.R.-R. and J.E.L.-R.; resources, R.R.-R.; data curation, J.E.G.-T. and R.R.-R.; writing--original draft preparation, J.E.G.-T. and R.R.-R.; writing--review and editing, R.R.-R., J.E.L.-R. and S.V.-R.; visualization, J.E.L.-R.; supervision, R.R.-R., J.E.L.-R. and J.L.G.C.; project administration, R.R.-R.; funding acquisition, R.R.-R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"CICES Research Center\" and \"The Council for Science and Technology of Mexico\" (CONACYT), No. CVU: 765196.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the large size of the dataset required to maintain a public repository.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Link (R)** & **Metrics** & **Link (R)** & **Metrics** & **Link (R)** & **Metrics** & **Link (R)** & **Metrics** \\ \hline
1,2 & 2,0,89 & 47,48 & 2,0,89 & 46,60 & 2,0,97 & 92,106 & 3