Configuring the Modified Method of Feasible Directions (MMFD) Technique

The Modified Method of Feasible Directions (MMFD) is a direct numeric optimization technique used to solve constrained optimization problems.

See Also
Modified Method of Feasible Directions (MMFD) Technique
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 MMFD.
  4. In the Optimization Technique Options area, enter or select the following:
    OptionDescription
    Max Number of Iterations An iteration consists of two phases. In the first phase, a plausible search direction is computed from the gradient of the objective function and constraints at the current design point. In the second phase, new designs are evaluated along the selected direction (cost: one run per design) until no improvements are found or until a constraint is violated. The two phases are repeated until the specified convergence requirements are met. This option controls how many of these pairs of phases will take place. The value type is integer. The default value is 40.
    Relative Gradient Step The relative finite difference step size to be used by the optimizer, when calculating gradients using finite difference techniques. The default value is 0.01 (1%).
    Minimum Absolute Gradient Step The smallest (minimum) absolute value of the finite difference step when calculating gradients. It prevents a step from being too small when a parameter value is near zero. The default value is 0.001.
    Absolute Convergence Criterion The absolute value of the termination criterion. If the objective does not change by more than this value in successive iterations, optimization is terminated. The value type is real. The default value is 0.001.
    Relative Convergence Criterion The relative value of the termination criterion. If the fractional (relative) change in objective value is smaller than the value of this criterion for several successive iterations, the optimization is terminated. The value type is real. The default value is 0.001 (0.1% of the objective value).
    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.
    Maximum 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.
  5. Click Ok to save your changes and to close the Optimization Editor.