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Optimetrics >
   Setting up an Optimization Analysis       


Setting up an Optimization Analysis

Following is the general procedure for setting up an optimization analysis. Once you have created a setup, you can Copy and Paste it, and then make changes to the copy, rather than redoing the whole process for minor changes.

1. Set up the variables you want to optimize in the Properties dialog box.

2. On the Product menu, point to Optimetrics Analysis, and then click Add Optimization.

The Setup Optimization dialog box appears.

3. Under the Goals tab, select an optimizer by clicking Sequential Non-Linear Programming, or Sequential Mixed Integer Non-Linear Programming, Quasi Newton, Pattern Search Optimizer, or Genetic Algorithm in the Optimizer pull-down list.

Selecting either the Quasi Newton or Pattern Search optimizers enables the display of the Acceptable Cost and Noise fields. These fields do not appear for the SNLP or SMINLP optimizers.

For the Quasi Newton and Patterns Search optimizers, under the Variables tab of the Setup dialog, selecting the Advanced button lets you specify a Min Step and Max Step. For the SNLP and SMINLP optimizers, under the Variables tab of the Setup dialog, selecting the Advanced button lets you specify values for Min focus and Max focus instead.

For the Genetic Algorithm optimizer, a Setup button is enabled. Click Setup to open the Advanced Genetic Algorithm Optimizer Options dialog.

4. Type the maximum number of iterations you want Designer to perform during the optimization analysis in the Max. No. of Iterations text box.

5. Select Setup Calculations to choose a solution for which to create a Cost Function.

6. Add a Cost Function by selecting the solution from the Cost Function box and clicking the Edit Goal/Weight button.

7. For the Quasi Newton and Pattern Search optimizers:

• Type the value of the cost function at which the optimization process should stop in the Acceptable Cost text box.

• Type the cost function noise in the Noise text box.

8. If you want to select a Cost Function Norm Type:

a. Check the Advanced options checkbox.

This displays the Cost Function Norm Type pull down menu.

b. Select one of L1, L2, or Maximum.

A norm is a function that assigns a positive value to the cost function.

For L1 norm the actual cost function uses the sum of absolute weighted values of the individual goal errors. For L2 norm (the default) the actual cost function uses the weighted sum of squared values of the individual goal error. For the Maximum norm the cost function uses the maximum among all the weighted goal errors. (For further details, see Explanation of the L1, L2, and Max Norms in Optimization.)

The norm type doesn’t impact goal setting that use as condition the “minimize” or “maximize” scenarios.

 

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Related Topics

Advanced Genetic Algorithm Optimizer Options




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