Using the Gradient Based Algorithm to Optimize Problems With Non Satisfied Constraints

You can use the Gradient based Algorithm to optimize Problems with non satisfied constraints.

Before you begin: Refer to the Sample topic to create the model you need to perform this scenario.
See Also
Optimization Editor
Specifying the Algorithm to Run
  1. Click Optimization to access the Optimization dialog box.
    The Optimization dialog box appears.
  2. Enter the parameters below in the Problem tab:
    Optimization Type Target Value
    Optimized Parameter Volume.1
    Target Value 0.8L
    Free Parameters

    xAyAxByB
    Inf. Range 40405040
    Sup. Range 8080200100

    Algorithm Gradient Algorithm With Constraints
    Termination Criteria
    • Maximum number of updates
    • Consecutive updates without improvements
    • Maximum Time (minutes)
    200
    50
    5
  3. Enter the following constraints in the Constraints tab:
    Constraint.1 Z**2 + Y**2 < 5000 mm2
    Constraint.2 Y**2 + Z**2 > 1000 mm2
  4. Click Run optimization. Enter the name of the representation and click OK.
  5. Once the optimization is over, redefine the ranges of the free parameters (see below):
    Free Parameters

  6. Click Run optimization. The generated values are much closer to the Target value.