.. _auto_curve_simulated_annealing_optimizer:

*FIND THE OPTIMIZE X BASED ON THE SIMULATED ANNEALING*
======================================================

Finds the optimal X value on a curve using the Simulated Annealing metaheuristic algorithm. Accepts a 2-D curve (X/Y points) and searches for the X that minimizes or maximizes the Y value (or drives it toward a target), making it useful for non-convex, noisy objective landscapes where gradient-based methods fail.

When to use
-----------

Classification: **process**.

Tagged: ``curve``, ``maximize``, ``metaheuristic``, ``minimize``, ``minmax``, ``normalization``, ``optimization``, ``simulated_annealing``.

Inputs
------

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

   * - Label
     - ID
     - Type
     - Default
     - Required
     - Description
   * - Curve
     - curve
     - vector
     - —
     - 
     - Input 2-D curve (X/Y point pairs) over which the optimizer will search for the optimal X value; must contain at least two points.
   * - Number Of Iterations
     - num_iterations
     - scalar
     - 100
     - 
     - Total number of simulated annealing iterations to perform; higher values improve solution quality at the cost of runtime (default: 100).
   * - Step Size
     - step_size
     - scalar
     - 0.01
     - 
     - Perturbation step size applied to X at each iteration; smaller values give finer local search while larger values allow broader exploration (default: 0.01).
   * - Initial Temperature
     - initial_temperature
     - scalar
     - 10.0
     - 
     - Starting temperature for the annealing schedule; higher values increase the probability of accepting worse solutions early, promoting global search (default: 10.0).
   * - Data Normalization Type
     - normalize
     - scalar
     - minmax
     - 
     - Normalization method applied to the curve data before optimization: 'minmax' (0–1 scaling), 'znorm' (zero-mean unit-variance), or 'false' to skip normalization (default: 'minmax').
   * - Target Value
     - target_value
     - scalar
     - 0
     - 
     - Target Y value the optimizer tries to reach; set to 0 (default) when purely minimizing or maximizing without a specific target.
   * - Objective
     - objective
     - scalar
     - minimize
     - 
     - Optimization direction: 'minimize' to find the X yielding the lowest Y, or 'maximize' to find the X yielding the highest Y (default: 'minimize').

Outputs
-------

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   :header-rows: 1
   :widths: 20 20 20 20

   * - Label
     - ID
     - Type
     - Description
   * - curve_simulated_annealing_optimizer_output_1
     - curve_simulated_annealing_optimizer_output_1
     - scalar
     - Optimal X value found by the simulated annealing algorithm — the point on the input curve that best satisfies the specified objective (minimize/maximize/target).

Disciplines
-----------

- data.curve.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=curve_simulated_annealing_optimizer <https://www.d3view.com/api/workflow/example?id=curve_simulated_annealing_optimizer>`_

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