# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 4 Methodology
The evaluation contains the box plot characteristics of the objective function values of the generated solution candidates (the smallest observation--sample minimum, lower quartile, median, upper quartile, and largest observation--sample maximum) calculated for each series. The evaluation also includes the objective function value of the best-found solution candidates in all optimisation experiments in the series; the range of provided function objective values during the optimisation experiment, and the number of simulation experiments until the termination criterion is met. * Step 16 to 17--Each simulation optimiser sends all the calculated evaluation results to the client after the completion of all performed series. Sending data also prevents the loss of data from simulation experiments performed on the simulation optimiser if the optimiser fails. These results can be used for the evaluation of the optimisation processes or the evaluation of the behaviour of the methods. * Step 18--We proposed different criteria which express the success or the failure of the optimisation method in different ways. Each criterion value is between [0,1] and it is \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Optimisation Method** & **Optimisation Method Parameter** & **Step** & **Lower Boundary** & **Upper Boundary** \\ \hline \multirow{8}{*}{Particle swarm optimisation} & Number of particles & \(10*n\) & \(2*n\) & \(42*n\) \\ \cline{2-4}  & Weight & 0.2 & 0.8 & \(1.4\) \\ \cline{2-4}  & Particle constant & 0.7 & 0.2 & 3 \\ \cline{2-4}  & Global constant & 0.7 & 0.2 & 3 \\ \cline{2-4}  & Reduce weight (Boolean) & 1 & 0 & 1 \\ \cline{2-4}  & Reducing weight constant & 0.1 & 0.1 & 0.5 \\ \cline{2-4}  & Life span & 20 & 30 & 90 \\ \cline{2-4}  & Leader age & 5 & 0 & 20 \\ \cline{2-4}  & Leader—maximum evaluation & 1 & 2 & 5 \\ \cline{2-4}  & Dimension & 1 & 2 & 5 \\ \cline{2-4}  & Reassigning gap & 3 & 2 & 20 \\ \cline{2-4}  & Parameter of calculated particle velocity & 0.1 & 0.2 & 0.8 \\ \cline{2-4}  & Type of strategy & 0 & 0 & 5 \\ \hline \multirow{8}{*}{Genetic algorithm} & Population minimum size & \(10*n\) & \(2*n\) & \(42*n\) \\ \cline{2-4}  & Population maximum size & \(10*n\) & \(2*n\) & \(42*n\) \\ \cline{2-4}  & Type of generation strategy & 1 & 0 & 1 \\ \cline{2-4}  & Number of generations & 5 & 1 & 6 \\ \cline{2-4}  & Type of selection & 1 & 0 & 3 \\ \cline{2-4}  & Selection tournament size & \(4*n\) & \(n\) & \(5*n\) \\ \cline{2-4}  & Crossover probability & 0.25 & 0.5 & 1 \\ \cline{2-4}  & Type of crossover & 1 & 0 & 4 \\ \cline{2-4}  & Crossover swap point index & \(\left\lceil\frac{TB-2}{2}\right\rceil\) & 0 & & \(2\left\lceil\frac{TB-2}{2}\right\rceil\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._calculated from the box plot characteristics--the smallest observation--sample minimum \(Q_{1}\), lower quartile \(Q_{2}\), median \(Q_{3}\), upper quartile \(Q_{4}\), and largest observation--sample maximum \(Q_{5}\). These characteristics are calculated for each series (the setting of the optimisation method parameters)--see Section 6 Evaluation.