Configuring the Pointer Automatic Optimization Technique

The Pointer technique can efficiently solve a wide range of problems in a fully automatic manner because of a proprietary control algorithm.

Before you begin: The Pointer-2 technique is superior to the Pointer technique, and you should always use Pointer-2 over Pointer. The original Pointer technique is retained only for compatibility with older models.
See Also
Pointer Techniques
Configuring the Technique and the Execution Options
  1. From the Flow section of the action bar, click Optimization and drop it on the process diagram.
  2. Double-click Optimization .
    The Optimization Editor appears.
  3. From the General tab's Optimization Technique list, select Pointer.
  4. In the Optimization Technique Options area, enter or select the following:
    OptionDescription
    Maximum allowable job time (hr) The time (hours) that the Pointer technique has to complete a job. The Pointer technique will use all the time you give it, even if it finds the global optimum early on. The longer it is stuck without finding an improvement, the more radical changes it will try. It accepts a real input greater than 0.0. The default value is 1.0.
    Average analysis time (sec) The average wall clock time (seconds) it takes to run a single simulation, including all Optimization Process Composer-related overhead (for file parsing, etc.). The Pointer technique takes the ratio of allowable job time and average simulation time to estimate how many simulations it will be able to do and adjusts its search strategy accordingly. You must enter a real value greater than 0.0. The default value is 1.0.
    Topography type The default topography type is nonlinear. The following options are available:
    • linear: the objectives and constraints are linear combinations of the design variables.
    • smooth: the output function generates a design space that is differentiable everywhere and contains no discrete steps but could still contain multiple local minima.
    • rough: the output function generates a design space that is not necessarily smooth but only contains small scale discontinuities or noise.
    • discontinuous: the output function generates a design space that could contain large scale discrete steps and points where the function is not differentiable.
    • If nonlinear: the only assumption made is that objectives and constraints are not linear combinations of the design variables (that is, the problem is not a linear one).
    • unknown (default): no assumptions have been made about the nature of the design space generated by the output function.
    Save Technique Log Most optimization techniques create a log file of information/messages as they run. This information can be useful for determining why an optimizer took the path that it did or why it converged. Some of these log files can get extremely large, so they are not stored with the run results by default. Select this option if you want to store the log with the run results (as a document) for later viewing.
    Max Failed Runs The maximum number of failed subflow evaluations that can be tolerated by the optimization technique. If the number of failed runs exceeds this value, the optimization adapter will terminate execution. To disable this feature, set this option to any negative value (for example, –1). When this option is set to a negative value, the optimization will continue execution despite any number of failed subflow runs.
    Failed Run Penalty Value The value of the Penalty parameter that is used for all failed subflow runs. The default value is 1⁢ × ⁢1030.
    Failed Run Objective Value The value of the Objective parameter that is used for all failed subflow runs. The default value is 1⁢ × ⁢1030.
    Use Fixed Random Seed If you select this option, the random number generator used by the optimization algorithm is seeded using the value specified in the corresponding text box.

    If you do not select this option, the random number generator is seeded from the clock time at the moment of execution, and every execution of the optimization will produce different design points.

    Random Seed Value All executions of the Optimization adapter will use the same sequence of random numbers and, therefore, will produce the same design points. This arrangement is useful for debugging the optimization process when it is necessary to reproduce the same sequence of design points.
  5. Click Ok to save your changes and to close the Optimization Editor.