.. _auto_dataset_simulated_annealing_optimizer:

*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
------

.. list-table::
   :header-rows: 1
   :widths: 20 20 20 20 20 20

   * - 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
-------

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   :widths: 20 20 20 20

   * - 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 <https://www.d3view.com/api/workflow/example?id=dataset_simulated_annealing_optimizer>`_

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   Auto-generated from <code>transformation</code> schema. Worker id: <code>dataset_simulated_annealing_optimizer</code>. Schema hash: <code>7a9f3205e2f3</code>. Hand-curated docs in <code>workerexamples/</code> override this page when present.
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