FIND THE OPTIMUM DESIGN BASED ON OBJECTIVE¶
Applies simulated annealing to find the optimal design point within a dataset by fitting a surrogate model (polynomial regression, Kriging, or curve interpolation) over the input/target columns and iteratively searching for the minimum or maximum objective. Use this worker when you need a stochastic, gradient-free optimizer to escape local minima across a design space defined by tabular data.
When to use¶
Classification: process.
Tagged: curve_interpolate, design_exploration, design_optimization, kriging, objective_function, optimization, polynomial_regression, simulated_annealing.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Dataset | dataset_1 | dataset | — | Input dataset (tabular) containing design variable columns and response/target columns; all subsequent column selections must refer to column names present in this dataset. | |
| Inputs | inputs | text | — | One or more dataset column names to treat as design input variables (X) fed into the surrogate model; select all independent variable columns. | |
| Targets | targets | text | — | One or more dataset column names to treat as response/target variables (Y) that the optimizer will minimize or maximize; select the objective-carrying columns. | |
| Number Of Iterations | num_iterations | scalar | 100 | Total number of simulated-annealing iterations to run; default is 100 — increase for higher-dimensional or noisy spaces at the cost of compute time. | |
| Step Size | step_size | scalar | 0.01 | Perturbation magnitude applied to input variables at each iteration (dimensionless fraction of the normalized input range); default 0.01 — reduce for fine local search, increase for broader exploration. | |
| Initial Temperature | initial_temperature | scalar | 10 | Starting temperature for the annealing schedule controlling the probability of accepting worse solutions early in the search; default 10 — higher values increase early exploration. | |
| Model Type | model_type | scalar | polynomialRegression | Surrogate model used to evaluate the objective between data points; choose ‘polynomialRegression’, ‘kriging’, or ‘curve_interpolate’ — default is ‘polynomialRegression’. | |
| Model Parameters | model_params | form-input-table | — | ||
| Shrink Factor | shrink_factor | scalar | 0 | ||
| Return Type | return_type | scalar | optimum | ||
| Data Normalization Type | normalize | scalar | minmax | ||
| Target Value | target_value | textarea | 0 | Target value for optimum | |
| Objective | objective | scalar | minimize | ||
| Grid Search | grid_search | scalar | no | ||
| Constraints | constraints | dataset | — | Dataset with constraints. The expected columns are needle, condition, target. Where needle is the column that is meet the condition specified by target. Ex needle=variable1 condition=gt and target=10.0 <a class=’btn btn-xs btn-default’ target=’_blank’ href=’https://www.d3view.com/docs/master/workflows/Glossary.html#datasetinput’> <i class=’fa fa-external-link’> </i> View more </a> | |
| Mathmodel | mathmodel_id | remote_lookup | — | ||
| STOP RATIO | stopping_criteria | scalar | 0.1 | When convergence is not improving after STOP_RATIO*NUM_ITERATIONS, the optimization is terminated. If STOP < 0, the optimization is stopped when convergence is not improving within abs(STOP_RATIO) steps |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| dataset_simulated_annealing_optimizer_output_1 | dataset_simulated_annealing_optimizer_output_1 | dataset | Output dataset containing the optimum design point(s) found by the annealing search, including the optimal input variable values and the corresponding predicted response value(s). |
Disciplines¶
- ai_ml.surrogate
- data.dataset.transform
- design_exploration.optimization
Runnable example¶
A runnable example is registered for this worker. Open the example workflow on the d3VIEW canvas: /api/workflow/example?id=dataset_simulated_annealing_optimizer
Auto-generated from transformation schema. Worker id: dataset_simulated_annealing_optimizer. Schema hash: 7a9f3205e2f3. Hand-curated docs in workerexamples/ override this page when present.