Parameter optimization is used to identify optimal settings for the inputs that you can control. Workspace searches a range of values for each input to find settings that meet the defined objective and lead to better performance of the system. After a simulation analysis, you can perform a parameter optimization or a sensitivity analysis. However, for engineering applications, perform the parameter optimization prior to a sensitivity analysis because changing the system settings is often easier than changing the variability of the inputs. For example, adjusting the temperature setting is easier than reducing the variability of the temperature.
When you perform a parameter optimization, Workspace searches for alternative input settings that optimize an output based on the objective and the search range you define.
Consider using search ranges that are as wide as possible to broaden the search area and increase your chances of meeting your objective. Do not exceed levels that are unfeasible or unsafe for your system. You can repeat the parameter optimization and see how changing the search range affects the estimates of performance.
Each time you repeat the simulation, the results will vary because the simulation is based on randomly selected values for the inputs.
After you analyze the results, you may want to return to the model and change inputs or outputs, then rerun it. This lets you test several possible scenarios so that you can get insight into the behavior of your system and make better decisions.