## 2 Related Work
In MCEAACO-QSRP [28], the authors designed a multi-objective optimization strategy based on adaptive ant colonies to guarantee the quality of service in the industrial Internet of Things (IoT), considering the QoS constraints of delay, security, and power consumption. [PERSON] et al. [13] proposed a routing algorithm based on a multi-objective genetic algorithm for the QoS of the optical burst communication network. The algorithm takes the parameters of the minimization of delay, bandwidth, and packet loss. As a summary of the reviewed works (Table 1), we can argue that the most implemented routing model to guarantee QoS is focused on the available bandwidth parameter [18; 19; 20; 21; 26; 27]. VSDN [22] and PRIME-Q [23] classified flows according to the multimedia flows' required bandwidth and delay parameters. PRIME-Q has the particularity of offering three options: the first is path selection with maximum available bandwidth; the second is path selection with the minimum delay; and the third is both network parameters as restrictions. Another approach is to reallocate resources when a congestion problem occurs [18; 19; 21; 27], where the parameter to consider when improving the QoS is the bandwidth available in the network, without considering the delay and packet loss that may occur on the links, except for OpenQoS [17], which obtains a better performance by giving higher priority to delay than bandwidth. On the other hand, multi-objective techniques have been implemented to meet the requirements of multiple QoS parameters such as bandwidth, delay, packet loss, and jitter [24; 25; 28; 29]. These methods focus on optimizing one objective function, while the other parameters satisfy the minimum requirements of the data streams. Our routing proposal differs mainly by considering the minimization of these parameters, which gives us a set of feasible and balanced solutions since they are non-dominated solutions. \begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Network Parameters** & **Path Selection** & **Domain** \\ \hline
[17] & Bandwidth, Delay, Jitter & Min delay and bandwidth as constraints & Single \\ \hline
[18] & Bandwidth & Min bandwidth & Single \\ \hline
[19] & Bandwidth & Max bandwidth & Inter-domain \\ \hline
[20] & Bandwidth & Max bandwidth & Single \\ \hline
[21] & Bandwidth & Max bandwidth & Single \\ \hline
[22] & Bandwidth, Delay, Jitter & Max bandwidth and delay as constraints & Single \\ \hline
[23] & Bandwidth, Delay & (1) Max bandwidth, (2) min delay, and (3) & Inter-domain \\ \hline
[24] & Bandwidth, Delay, Jitter, Packet Loss & Application requirements as constraint & Inter-domain \\ \hline
[25] & Bandwidth, Delay, Jitter, Packet Loss & Application requirements as constraint & Single \\ \hline
[26] & Bandwidth & Bandwidth as constraint & Single \\ \hline
[27] & Bandwidth & Bandwidth as constraint & Inter-domain \\ \hline
[28] & Delay, Security, Power Consumption & Application requirements as constraint & Single \\ \hline
[13] & Bandwidth, Delay, Packet Loss & Min delay and application requirements & Single \\  & as constraints & Single \\ \hline MOCell Routing & Bandwidth, Delay, Packet Loss & Min (bandwidth, delay, and packet loss) & Inter-domain \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of QoS routing methods.