One-Click Optimization (OCO)

This node provides an efficient hybrid optimization strategy that comes with only one major setting to be tuned, the maximum number of design evaluations. Depending on the type and number of input parameters and the defined optimization criteria, the optimizer automatically selects the most suitable optimization algorithms with their most appropriate settings to solve the optimization problem. The ability to dynamically switch between optimization algorithms and to run multiple algorithms simultaneously makes OCO one of the most reliable and efficient optimization strategies. OCO is a surrogate assisted optimization strategy, using capabilities of the Metamodel of Optimal Prognosis (MOP) for function approximation to significantly speed up the optimization process.

Node Settings

To access the options shown in the following table, double-click the One-Click Optimization system on the Scenery pane and in the Settings tab, click Show advanced settings.

Name	Default Value	Description
Maximum number of design evaluations	200	Maximum number of design evaluations to compute before stopping.
Advanced settings
Use MOP	On	Accelerate convergence and save the number of evaluations using the MOP. Deactivate when dealing with computational inexpensive evaluations.
Stop at the first feasible design	False	Stops the optimizer when at least one feasible design has been evaluated.
Stop after the given number of design evaluations without improvement	40	Stops the optimization when no improvement of the best individual is seen after this number of design evaluations and the minimum number of design evaluations has been generated.
Fixed number of design evaluations per iteration	Auto	Defines the number of design evaluations generated per iteration. When set to `Auto` (or no value), the number of design evaluations can vary from one iteration to another one. The maximum allowable value is `999`.
Expert settings: Initial designs
Use start designs only	False	When selected, the first iteration uses only start designs as initial design evaluations. Number of initial design evaluations is deactivated.
Number of initial design evaluations	Auto	Defines the number of design evaluations generated for the first iteration. When set to Auto (or no value), the number of design evaluations depends on the number of input parameters and the maximum number of design evaluations. The number of design evaluations must be greater than the number of input parameters and 10 percent of the maximum number of design evaluations. The number of design evaluations must be less than (maximum number of design evaluations - 10 ) and 90 percent of the maximum number of design evaluations. If at least one of the previous conditions is not satisfied, then OCO adjusts automatically the number of initial design evaluations on the fly.
Expert settings: Convergence test
Minimum number of design evaluations before the convergence test	100	Defines the minimum number of designs to evaluate before stopping the algorithm due to stagnation.
Expert settings: MOP
Activate dimension reduction	On	Filters the most relevant input variables to speed-up the convergence of the optimization process.
Dynamic selection	On	When selected, OCO activates/deactivates the most suitable metamodels according to the number of submitted designs, based on the default pre-selection of metamodels. You can modify the pre-selection by enabling/disabling any metamodel. When cleared, all selected metamodels are used independently of the number of submitted designs.
Isotropic Kriging	On	Selects Isotropic Kriging as tested metamodel to create a MOP.
Anisotropic Kriging	Off	Selects Anisotropic Kriging as tested metamodel to create a MOP.
Moving least squares	On	Selects Moving least squares as tested metamodel to create a MOP.
Polynomials	On	Selects Polynomials as tested metamodel to create a MOP.
Deep Feed Forward Network	Off	Selects Deep Feed Forward Network as tested metamodel to create a MOP.
Deep Infinite Mixture Gaussian Process	Off	Selects Deep Infinite Mixture Gaussian Process as tested metamodel to create a MOP.
Genetic Aggregation Response Surface	Off	Selects Genetic Aggregation Response Surface as tested metamodel to create a MOP.
Support Vector Regression	Off	Selects Support Vector Regression as tested metamodel to create a MOP.
Expert settings: Optimization methods competition
Maximum number of concurrently competing methods	2	Maximum number of allowable optimizations in parallel.
Non-linear Programming by Quadratic Lagrangian (NLPQL)	On	Defines NLPQL as the allowable optimization method.
Downhill Simplex Method	On	Defines Downhill Simplex Method as the allowable optimization method.
Mixed-Integer Sequential Quadratic Programming (MISQP)	On	Defines MISQP as the allowable optimization method.
Adaptive Response Surface Method (ARSM)	On	Defines ARSM as the allowable optimization method.
Adaptive Metamodel of Optimal Prognosis (AMOP)	Off	Defines AMOP as the allowable optimization method.
Evolutionary Algorithm (EA)	On	Defines EA as the allowable optimization method.
Particule Swarm Optimization (PSO)	On	Defines PSO as the allowable optimization method.
Stochastic Design Improvement (SDI)	On	Defines SDI as the allowable optimization method.
Covariance Matrix Adaptation (CMA)	On	Defines CMA as the allowable optimization method.

Additional Options

To access the options shown in the following table, in any tab, click Show additional options.

Option	Description
Limit maximum in parallel	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.
Auto-save behavior	Select one of the following options: No auto-save Actor execution finished Every n-th finished design (then select the number of designs from the text field) 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.

Option

Description

Limit maximum in parallel

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.

Auto-save behavior

Select one of the following options:

No auto-save
Actor execution finished
Every n-th finished design (then select the number of designs from the text field)

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.

Logging

This node creates a log file named OCO_logfile.txt. The log file has two levels, default and verbose. The default log captures initial settings at the start of the algorithm, internal activation and deactivation events, current number of evaluations, and so on. The verbose log captures all of the information of the default log and also logs which optimization algorithms were chosen for use.

To change the log level to verbose, create an OCO_VERBOSE_LOG environment variable.