OPTIMIZE, Multi-parameter optimization

Before performing this analysis, (actual OPTIMIZE.SCP file), you must select the parameters to vary using the Tolerance/Sweep/Optimize tap in the parts properties dialog. The values entered for the optimize tolerance are similar to the ones used for Monte Carlo. The set a range of values for which optimization is constrained. For best results, there should be a minimum somewhere within the selected range. The values you select won't be remembered after saving your document. Next, you must create an objective function. To do this use the simulation control (ICAPS) dialogs measurement tab. You should have only one measurement in the test configuration. It doesn't matter what it's named. Then make sure the correct optimize template is selected, and the radio button above the question mark is selected. The Data reduction should be set to Interactive and the Script checkbox must be checked
only if you have scripted measurements. .Then Select one of the appropriate Test Configurations and press Simulate Selections. Progress will be shown in the IsSpice output window.

Optimization is performed using algorithms that minimize the objective function. Your 2 main challenges are to ask the "right" question with the objective function and to bracket the solution space with the "tolerance" placed on the parameter that can vary. Circuits\Snubber\Snubber.dwg, Circuits\sprobe\sprobe.dwg and Circuits\Power\FwdTemplate\FwdTemplate.dwg all have objective functions that can be used for optimization.

The optimization process itself consists of measuring the objective function for a set of parameter values, and then finding the parameter value that minimizes objective function. Optimize.scp is a single pass version and Optimize2.scp is a 2-pass version. If you are doing single parameter optimization to select a component value, then optimize should work. If you are doing multi-parameter optimization, use optimize2. You load either of these into IsEd5 and modify them; constants.maxiter, changes the number of iterations. The algorithm uses polynomial regression to make a high order curve fit so that it is possible to find a minimum value in the presence of local minimum values. The pure mathematical versions usually perform more iterations; but, a single iteration usually converges to within the component tolerance value, making further passes unnecessary.