# Data Reduction of Digital Twin Simulation Experiments Using Different Optimisation Methods
## 5 Simulation Optimiser
If this database contains this record, the simulation optimiser downloads these encrypted data by the hashing algorithm and decrypts them; otherwise, the simulation is run with the concrete settings of the simulation model input parameters. 2. The simulation optimiser stores the specified number of records (executed simulation runs and calculated objective function values) in the local memory--this option is used when the simulation optimiser can use the limited available memory. The optimiser scans its internal computer memory first to find a specific record (the concrete settings of the simulation model input parameters). If it does not find this record in the internal memory, it searches the SQL server for this record. If the record is not found, the simulation optimiser runs the simulation experiment with the concrete settings of the simulation model input parameters. 3. The simulation optimiser stores all the performed optimisation experiments (all records)--the logic is the same as the previous variant except that all the simulation runs in all optimisation experiments are held in the computer's memory. The simulation optimiser downloads all records from the SQL server database. This approach will cause a certain time delay before the first simulation run is started, but it will save time spent on the communication between the SQL server and the simulation optimiser. 4. Step 18--The simulation optimiser continuously displays the state of the optimisation experiment. It displays the value of the objective function of the proposed solution candidate; decision variables values (the settings of the simulation model input parameters of the proposed solution); the value of the objective function and the number of the simulation experiments in which the best solution candidate was found by the optimisation method; the value of the objective function of the current solution; the number of the current simulation experiment; simulation experiment computation time; the number of loaded/saved simulation experiments in the internal or external database, etc. ; see Figure 13. The simulation optimiser also calculates the evaluation box plot characteristics of objective function values of the best-found solution candidates, all generated solution candidates, and the number of performed simulation experiments for each series. 5. Step 21--If any of the termination criteria are met, the optimisation experiment is terminated. ## 6 Evaluation

Many research papers use the mean and the standard deviation to evaluate the performance of the optimisation methods. This evaluation criterion is sufficient for commonly used testing functions, e.g., [PERSON]'s function, [PERSON]'s function, [PERSON]'s function, etc. [78] where the global optimum is known. We proposed different evaluation criteria [33]. Each criterion value is between 0 and 1, and it is calculated from the box plot characteristics for the smallest observation, namely, sample minimum Q1, lower quartile Q2, median Q3, and upper quartile Q4, and for the largest observation, sample maximum Q5. These characteristics are calculated for each series, defining the setting of the optimisation method parameters. As we wanted to compare the efficiency of the implemented optimisation methods, we mapped all the solution candidates in the search space to identify the global optimum of the objective function of the AGV's DT. ### Quality of the Found Solution Candidate

Figure 11 shows the calculated quartiles characteristics from all the performed series (replicated optimisation experiments with the concrete setting of the optimisation method) of all the optimisation methods performed on the AGV's digital twin. There are four winners in this category--differential evolution, downhill simplex, self-organising migrating algorithm (SOMA), and genetic algorithm.