Initial number of samples to use. This number must be greater than the number of enabled input parameters.

The minimum recommended number of initial samples is 10 times the number of enabled input parameters. The larger the initial sample set, the better your chances of finding the input parameter space that contains the best solutions.

Number of samples to iterate and update with each iteration.

This number must be greater than the number of enabled input parameters but less than or equal to the number of initial samples.

Stop criterion. Maximum number of iterations that the algorithm is to execute. If this number is reached without the optimization having reached convergence, iterations stop.

This also provides an idea of the maximum possible number of samples that are needed for the full cycle, as well as the maximum possible time it can take to run the optimization. For example, the absolute maximum number of samples is given by: Number of Initial Samples + Number of Samples Per Iteration * (Maximum Number of Iterations - 1).

Convergence criterion. Percentage value that represents the ratio of the number of desired Pareto points to the number of samples per iteration. When this percentage is reached, the optimization is converged. For example, a value of 70 with Number of Samples Per Iteration set to 200 would mean that the optimization should stop once the resulting front of the MOGA optimization contains at least 140 points. Of course, the optimization stops before that if the maximum number of iterations is reached. If the Maximum Allowable Pareto Percentage is too low (below 30), the process can converge prematurely. If the value is too high (above 80), the process can converge slowly. The value of this property depends on the number of parameters and the nature of the design space itself.

Using a value between 55 and 75 generally works well for most problems.

Convergence criterion. Percentage value that represents the stability of the population based on its mean and standard deviation.

This criterion allows you to minimize the number of iterations performed while still reaching the desired level of stability. When the specified percentage is reached, the optimization is converged. The default percentage is 2. To not take the convergence stability into account, set to 0

Advanced option for specifying the probability of applying a mutation on a design configuration.

The value must be between 0 and 1. A larger value indicates a more random algorithm. If the value is 1, the algorithm becomes a pure random search. A low probability of mutation (<0.2) is recommended.

Advanced option for specifying the probability with which parent solutions are recombined to generate offspring solutions.

The value must be between 0 and 1. A smaller value indicates a more stable population and a faster (but less accurate) solution. If the value is 0, the parents are copied directly to the new population. A high probability of crossover (>0.9) is recommended.

Defines the type of crossover for discrete parameters.

Three crossover types are available: One-Point, Two-Point, and Uniform. According to the type of crossover selected, the children are closer to or farther from their parents. The children are closer for One Point and farther for Uniform. The default is One Point.

Defines the type of sampling.

Choices are:

Specifies whether the random number generator is seeded with the same value each time that you generate samples.

Only available when Screening is used as Type of Initial Sampling.

Advanced option for specifying the maximum number of cycles for the optimal space filling algorithm.

Only available when Optimal Space Filling is used as Type of Initial Sampling.

Advanced option for specifying the value for initializing the random number generator invoked internally by Optimal Space Filling sampling.

The value must be a positive integer. This property allows you to generate different samplings by changing the value or to regenerate the same sampling by keeping the same value.

Only available when Optimal Space Filling is used as Type of Initial Sampling.

Controls the resource usage of nodes in the system.

When the check box is cleared (default), a value is chosen to ensure the best possible utilization of the child nodes. When the check box is selected, set the value manually to specify how many designs are sent to child nodes, limiting the maximum degree of parallelism for all children. Ansys recommends keeping the check box clear.

Select one of the following options:

The project, including the database, is auto-saved (depending on defined interval) after calculating this node/system (either when the calculation succeeds or fails).

By default, all parametric and algorithm systems have Every nth finished design 1 design(s) selected, all other nodes have No auto-save selected.