## 4 Experiments and Results  
The experimental setting was as follows. For each experiment, 700 runs were executed for each algorithm (micro artificial immune system (MAIS), simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE)) with a stop criterion of one million function evaluations (FEs). Our MAIS used a population of five individuals. For the other algorithms, the size of the population was forty. The settings used for the two functions were the same (see Table 1). The MAIS and all other algorithms were programmed in C and compiled by using g++ -OG. The hardware used was a heterogeneous cluster with 16 machines using Intel Core2 Quad processors Q9400 running at 2.66 GHz and 4 GB of memory. The cluster was administrated by a HTCondor 8.2.1. The total computing time was 200 hours of cluster time. The initial solutions for optimization were defined by the initial state of all the algorithms. The cluster it is located at Mexico City, Mexico, and is the 3 rd cluster version. Two areas of study were taken as reference to offer a proposal as close to reality as possible. The research was done on the _Avenida Revolucion_ area and _Avenida de los Insurgentes_ in Mexico City, see Figure 2a,b. The view of the area in OSM and SUMO is shown in Figure 3. For the simulation, 15 traffic lights were considered, with 66 phases in the changes of its lights, covering 0.66 km\({}^{2}\). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameters** & **Value** \\ \hline \multirow{3}{*}{Micro artificial immune system (MAIS)} & Population & 5 \\  & Clone limit & 15 \\  & Selection & Ranking \\  & Mutation & Based on affinity \\ \hline \multirow{3}{*}{Simulated annealing (SA)} & Population & 1 \\  & Freezing factor & 0.995 \\  & Disturb & Round [\(-\)5, 5] \\  & Disturb rate & 10\% \\ \hline \multirow{3}{*}{Genetic algorithm (GA)} & Population & 40 \\  & Selection & Binary tournament \\  & Crossing type & SPX \\  & Crossing probability & 100\% \\ \hline \multirow{3}{*}{Particle swarm optimization (PSO)} & Population & 10 \\  & Local coefficient & 2.06 \\ \cline{1-1}  & Global coefficient & 2.06 \\ \cline{1-1}  & Inertia & 0.5 \\ \hline \multirow{3}{*}{Differential Evolution (DE)} & Population & 40 \\ \cline{1-1}  & F & 0.4 \\ \cline{1-1}  & CR & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Algorithm setting. From the data obtained so far, it can be seen that the PSO (2.682) and MAIS (2.721) algorithms obtained better results, in terms of average fitness, than the rest, but PSO has a slightly bigger standard deviation, 0.060, compared to SA and MAIS (with 0.040). However, it is important to note that the MAIS algorithm has the best average value after PSO, see Table 2, consuming less resources in carrying out operations, but with almost the same time when executing SUMO, and MAIS has a better standard deviation, meaning that it has less variation in the results, see Table 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **SA** & **GA** & **MAIS** & **PSO** & **DE** \\ \hline Average waiting (minutes) & 1.6 & 2.2 & 1.3 & 1.4 & 1.7 \\ Journey time (minutes) & 10.6 & 11.2 & 10.3 & 10.4 & 10.7 \\ Average distance traveled (kilometers) & 3.58 & 3.85 & 3.52 & 3.50 & 3.61 \\ Average speed (kilometers per minute) & 0.337 & 0.343 & 0.341 & 0.336 & 0.337 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average results for travel metrics for both experiments. Figure 3: Detailed study area. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Number of Vehicles**} \\ \cline{2-5}
**Number of Traffic Logics (NTL)** & \multicolumn{5}{c}{**100**} \\ \cline{2-5}  & SA & GA & MAIS & PSO & DE \\ \hline
66 phases & 2.899 & 2.943 & 2.721 & 2.682 & 3.024 \\ Standard dev. & 0.040 & 0.0606 & 0.040 & 0.063 & 0.113 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average fitness value obtained by the algorithms for the first experiment set. Figure 2: Study area, “Av. Insurgentes” Street in Mexico City.