COMPUTE PARETO FRONT OPTIMAL FROM PROVIDED DATASET¶
Computes the Pareto-optimal point from a multi-objective dataset by filtering dominated solutions across the specified objective columns. Supports min/max criteria per objective, optional design constraints, target values, and optional meta-model fitting (polynomial or RBF) to refine the optimal point beyond the existing discrete samples.
When to use¶
Classification: process.
Tagged: design_exploration, dominance, meta_model, multi_objective, optimization, pareto, pareto_front, pareto_optimal.
Inputs¶
| Label | ID | Type | Default | Required | Description |
|---|---|---|---|---|---|
| Pareto Dataset | pareto_dataset | dataset | — | Input dataset containing the design space; each row is a design point and columns include the objective and input variables to evaluate for Pareto optimality. | |
| Columns | columns | scalar | — | Comma-separated list of column names from the dataset that represent the objectives to be optimized; must match column headers exactly (dynamic list populated from pareto_dataset). | |
| Criteria | criteria | scalar | min | Comma-separated optimization direction per objective column (e.g. ‘min,max’); number of entries must equal the number of columns specified — defaults to ‘min’ for all objectives. | |
| Constraints | constraints | dataset | — | Optional dataset defining inequality/equality constraints on design variables used to filter infeasible points before Pareto front computation; leave empty if no constraints apply. | |
| Target Values | targets | textarea | — | Optional target values for each objective specified as colon-separated key-value pairs (e.g. ‘mass:20,stress:30’); used to bias the optimal-point selection toward desired values. | |
| Fit Meta Model | fit_meta_model | select | — | Meta-model type to fit over the Pareto front for refining the optimal point beyond discrete samples; choose ‘no’ to return the best existing point, or ‘polynomial_1’, ‘polynomial_2’, RBF, etc. to interpolate a potentially improved optimum. | |
| Inputs if a Meta-model based Optimum is Chosen | inputs | scalar | — | Optional dataset or column list of input/design variables (distinct from objectives) used when fitting the meta-model to map design space to objective space. |
Outputs¶
| Label | ID | Type | Description |
|---|---|---|---|
| dataset_compute_pareto_front_optimal_output_1 | dataset_compute_pareto_front_optimal_output_1 | dataset | Single-row dataset representing the identified Pareto-optimal design point, including all original columns and the computed optimal values after applying the selected criteria, constraints, targets, and optional meta-model. |
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_compute_pareto_front_optimal
Auto-generated from transformation schema. Worker id: dataset_compute_pareto_front_optimal. Schema hash: 7a5fbfd32fa7. Hand-curated docs in workerexamples/ override this page when present.