## 4 Results  
In this section, the MA optimisation for the TLP in the hostelry application proposed is analysed and compared to the GA developed in previous studies [37]. The MA and every simulation was coded and executed in the Python software environment. All simulations were performed with an Intel(R) i7 2.4 GHz of CPU and 16 GB of RAM.  
The presented results were obtained from the same simulation parameters, shown in Table 2.  
Furthermore, the LS hyperparameters selected for the MA simulations are shown in Table 3.  
Two different configurations have been analysed for the Esla Salon. The first configuration is a 18 table distribution. It is deduced from previous simulations that this is the maximum number of tables to be positioned inside the proposed scenario, thus, the first configuration represents the maximum capacity problem.  
On the other hand, the second configuration is a 15 table distribution. In this analysis, we aim to obtain the table distribution that optimises the available distance among tables with a suboptimal capacity, therefore, a maximum dispersion problem.  
Furthermore, multiple tests have been executed with the GA and MA with the optimisation hyperparameters shown in Tables 2 and 3, being the best solution of each configuration selected for comparison. Figure 6 shows the fitness value evolution of every proposed configuration throughout the generations.  
\begin{table}
\begin{tabular}{c c} \hline
**Algorithm Hyperparameters** & **Value** \\ \hline Stop Criteria & 150 Generations or 90\% Population Equal \\ Crossover Methodology & Multipoint \(-3\) Crossover Points \\ Selection Methodology & Tournament 3 \\ Number of Individuals & 200 \\ TLP Points & 263,169 \\ Number of Possible Solutions & \(1.39\times 10^{92}\) / \(7.64\times 10^{75}\) (18/15 tables) \\ Elitism Percentage & 10\% \\ Mutation Percentage & 10\%/10\% \\ Parameter \(\kappa\) & 100 \\ Parameter \(\rho\) & 4.36 m \\ Parameter \(\lambda\) & 1.2 \\ \hline \end{tabular}
\end{table}
Table 2: MA and GA hyperparameters used for all the presented simulations. All values were adjusted experimentally based on the methodology introduced in [75].  
\begin{table}
\begin{tabular}{c c} \hline
**LS Hyperparameter** & **Value** \\ \hline LS Frequency & 1/5 Generations \\ LS Depth & 12 \\ LS Intensification & 20 \\ Parameter \(\tau\) & 7 \\ Parameter \(\delta\) & 12 \\ Parameter \(\zeta\) & \(-200\) \\ Parameter \(\varepsilon\) & 6 \\ \hline \end{tabular}
\end{table}
Table 3: LS Hyperparameters used for the MA simulations.  
The obtained distance distribution shown in Table 4 satisfies all measures and restrictions established by the Spanish legislation for the hostlery sector. Therefore, the achieved solutions represent safe and valid table arrangements for restaurants and other hostlery business in Spain.  
Moreover, the flexibility of this methodology exceeds the distance restrictions for the COVID-19 pandemic, it could be possible to request a certain distribution of tables for daily exploitation of the spaces. Other restrictions such as Line-Of-Sight to a particular point or distribution of points (i.e., theater or work tables) represent significant tasks for daily managing these areas, and a flexible algorithm like the one proposed in this paper represents a potential solution to these problems.