Problem definition

To define the optimization problem you must:

• specify the input parameters,

• set an acceptable range for these parameters,

• specify the objective to reach,

• specify the constraints to be respected.

This is done using the Study parameter definition pane of the optimization setup area. It consists of two tabs containing a list of inputs and outputs.

Figure 84: List of inputs

Figure 85: List of outputs

For both lists, the first column contains the name you gave to each parameter in the Parameters tab.

Inputs

You must tick the check boxes for the input parameters you want to involve in the optimization process (the input parameters you want Simcenter Amesim to vary). When the NLPQL technique is selected, it is not possible to tick the lines corresponding to discrete parameters. Remember you must use the Genetic algorithm if you want to use discrete parameters.

When a check box is not ticked, the value of the input remains constant during the optimization process. The value is specified in the Default value column. Lower bound and Upper bound columns are ignored for these parameters.

Simcenter Amesim varies the values of the other inputs within the range specified by Lower bound and Upper bound columns.

For the NLPQL algorithm you must specify a starting value for all the inputs to use. In this case, the default values are used as starting values. Default values are ignored in GA optimizations.

Outputs

Tick the Objective check boxes to set the corresponding output as an objective. The optimization algorithm will make the sum of the absolute value of all the objective outputs and will try to make it as small as possible.

Using the Constraint check boxes and the Lower bound and Upper bound columns, you can set constraints on several outputs.

For instance in the Figure 85 figure, we want to minimize the absolute value of BodyAcc while keeping FinalDisp lower than 0.002.

An output is saved in the log file if it is an objective or if it is under constraints.