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HS-1710: Simple Optimization Study

HS-1710: Simple Optimization Study

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HS-1710: Simple Optimization Study

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This tutorial demonstrates how to optimize a simple function defined using a Templex template. The base input template defines two input variables, DV1 and DV2, labeled X and Y, respectively.  The objective of the optimization is to minimize X + Y with the constraint 1/X + 1/Y – 2 < 0.

Before running this tutorial, you must complete tutorial HS-1010: Simple DOE Study (or HS-1700: Simple DOE Study, HS-1705: Simple Fit Study) or you can import the archive file HS-1705.hstx, available in <hst.zip>/HS-1710/.

hmtoggle_plus1greyStep 1: Run an Optimization Study
1.In the Explorer, right-click and select Add Approach from the context menu.
2.In the HyperStudy - Add dialog, select Optimization and click OK.
3.Go to the Select Input Variables step.
4.Review the input variable's lower and upper bound ranges.
5.Go to the Select Output Responses step.
6.Click Add Objective.
7.In the HyperStudy - Add dialog, add one objective.
8.Define the objective.
a.Set Type to Minimize.
b.Set Apply On to Response 1 (r_1).

hs_1710_objective

9.Click the Constraint tab.
10.Click Add Constraint.
11.In the HyperStudy - Add dialog, add one constraint.
12.Define the constraint.
a.Set Apply On to Response 2 (r_2).
b.Set Bound Type to <= (greater than or equal to).
c.In the Bound Value column, enter 0.0.

hs_1710_constraint

13.Click Apply.
14.Go to the Specifications step.
15.In the work area, set the Mode to Adaptive Response Surface Method (ARSM).
Note:Only the methods that are valid for the problem formulation are enabled.
16.Click Apply.
17.Go to the Evaluate step.
18.Click Evaluate Tasks.
19.Optional. Click the different tabs in the Evaluate step to monitor the progress of the Optimization.
20.After the optimization has finished, review the optimization history first.
21.With many of the algorithms (SQP, GA, …), each iteration requires many evaluations.
•Evaluation (plots or tables) show all runs performed during the optimization.
•Iteration (plots or tables) show the optimization iterations.

 

The Iteration History tab uses color coding to indicate which design are feasible, optimal, and violated.

•White background/black font indicates the design is feasible.
•White background/red font indicates the design is violated.
•White background/orange font indicates the design is acceptable, but at least one constraint is near violated.
•Green background/white font indicates the design is optimal.
•Green background/orange font indicates the design is optimal, but at least one constraint is near violated.

hs_1710_07

22.The Evaluation Plot tab displays charts for all of the entities in the optimization (input variables, output responses, objective functions, constraints) against the iteration.
•Select entities to plot using the Channel selector in the left pane.
•Plot multiple entities in separate windows by clicking multi_plot.

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23.Go to the Post processing step.

 
hmtoggle_plus1greyStep 2: Post-Processing of an Optimization Study

In the Post processing step of an Optimization approach, you can access additional tools to review the results. Use the Integrity, Distribution, and Scatter 2D/3D tabs to compare and analyze designs.

1.Click the Integrity tab to analyze statistics of the optimization study.

hs_1710_09

 

 

 

See Also:

HS-1700: Simple DOE Study

HyperStudy Tutorials