Optimizer Overview

The optimizer may be used to find optimal parameter settings for the modeled structure. Therefore, it is necessary to select the parameters that may be varied during the optimization process. The optimizer tries to minimize a function — the goal function. This function depends on the parameters selected for variation by the optimizer. Therefore, at least one goal must be defined that is evaluated while the optimizer is running. The goals for an optimization are always based on result templates. The optimizer tries to minimize the goals by varying the selected parameters.

Algorithm type

There are four different types of local and three different types of global optimizers available. The local ones are the Trust Region Framework, the Nelder Mead Simplex, the Interpolated Quasi Newton, and the Classic Powell optimizer. The global ones are the CMA Evolutionary Strategy, a Genetic and a Particle Swarm optimization algorithm. By using a statistical model CMA Evolutionary Strategy improves it's performance without sacrificing it's global optimization approach.

You may select the optimizer type from the Optimizer - Settings property page. More information about the optimization strategies can be found at the Optimizer - Interpolation of Primary Data page and on the Optimizer - Global Algorithm Settings page.

Parameters

The optimizer is a multidimensional optimizer. Thus, it is possible to simultaneously optimize several parameters. The parameters selected for optimization must be bounded by upper and lower limits. You may select the parameters for optimization from the Optimizer - Settings property page.

If you select the Interpolated Quasi Newton optimizer, it is necessary to set the number of samples N per parameter. Thus, the parameter range defined by the upper and lower limits is divided into (N – 1) sections. Within each section the primary data is interpolated.

Goals

The optimizer needs at least one goal that is evaluated while the optimizer is running. The goal value is calculated for each parameter configuration the optimizer sets during the optimization process. The goal value effects the further parameter configurations of the optimization process. Goals can be defined from the Optimizer - Goals property page.

Please note that if the sum or the maximum of all goal values evaluated while the optimizer is running is expected to be greater than zero. If it becomes less than or equal to zero, the optimizer finishes, assuming to have found the optimum.

Information / Optimizer logfile

While the optimizer is running, information about the course of the parameter settings and the evaluated goal values are displayed on the Optimizer - Info property page. Additional information is summarized in the optimizer logfile. You may display the contents of the optimizer logfile after the optimizer has finished by clicking Simulation: SolverLogfileOptimizer Logfile.

In addition, the course of the parameter variation and the goal values may be plotted by selecting the respective entries in sub folders of 1D Results/Optimizer in the Navigation Tree.

How to start the optimizer

You can launch the optimizer dialog box by choosing Simulation: SolverOptimizer from the main menu or by pressing the Optimize button in the respective solver dialog box.

Distributed computing

It is possible to increase the performance of the optimizer by running the selected solver for different parameter configurations using several computers simultaneously. To enable this feature press the Acceleration... button on the lower edge of the dialog. For more information on distributed computing, see the distributed computing overview.