[ENH] add scipy optimization backend

docs/source/_snippets/user_guide/optimizers.py

@@ -225,6 +225,117 @@ def objective(params):
 # [end:optuna_tpe]
 
 
+# ============================================================================
+# Scipy Backend
+# ============================================================================
+
+# [start:scipy_imports]
+from hyperactive.opt.scipy import (
+    ScipyDifferentialEvolution,  # Global: population-based
+    ScipyDualAnnealing,          # Global: simulated annealing variant
+    ScipyBasinhopping,           # Global: random perturbations + local search
+    ScipySHGO,                   # Global: finds multiple local minima
+    ScipyDirect,                 # Global: deterministic DIRECT algorithm
+    ScipyNelderMead,             # Local: simplex-based
+    ScipyPowell,                 # Local: conjugate direction method
+)
+# [end:scipy_imports]
+
+
+# Scipy uses continuous search spaces (tuples instead of arrays)
+scipy_search_space = {
+    "x": (-5.0, 5.0),
+    "y": (-5.0, 5.0),
+}
+
+
+# [start:scipy_differential_evolution]
+from hyperactive.opt.scipy import ScipyDifferentialEvolution
+
+optimizer = ScipyDifferentialEvolution(
+    param_space=scipy_search_space,
+    n_iter=100,
+    experiment=objective,
+    strategy="best1bin",
+    random_state=42,
+)
+# [end:scipy_differential_evolution]
+
+
+# [start:scipy_dual_annealing]
+from hyperactive.opt.scipy import ScipyDualAnnealing
+
+optimizer = ScipyDualAnnealing(
+    param_space=scipy_search_space,
+    n_iter=100,
+    experiment=objective,
+    random_state=42,
+)
+# [end:scipy_dual_annealing]
+
+
+# [start:scipy_basinhopping]
+from hyperactive.opt.scipy import ScipyBasinhopping
+
+optimizer = ScipyBasinhopping(
+    param_space=scipy_search_space,
+    n_iter=50,
+    experiment=objective,
+    minimizer_method="Nelder-Mead",
+    random_state=42,
+)
+# [end:scipy_basinhopping]
+
+
+# [start:scipy_shgo]
+from hyperactive.opt.scipy import ScipySHGO
+
+optimizer = ScipySHGO(
+    param_space=scipy_search_space,
+    n_iter=3,
+    experiment=objective,
+    n=50,
+    sampling_method="simplicial",
+)
+# [end:scipy_shgo]
+
+
+# [start:scipy_direct]
+from hyperactive.opt.scipy import ScipyDirect
+
+optimizer = ScipyDirect(
+    param_space=scipy_search_space,
+    n_iter=200,
+    experiment=objective,
+    locally_biased=True,
+)
+# [end:scipy_direct]
+
+
+# [start:scipy_nelder_mead]
+from hyperactive.opt.scipy import ScipyNelderMead
+
+optimizer = ScipyNelderMead(
+    param_space=scipy_search_space,
+    n_iter=200,
+    experiment=objective,
+    random_state=42,
+)
+# [end:scipy_nelder_mead]
+
+
+# [start:scipy_powell]
+from hyperactive.opt.scipy import ScipyPowell
+
+optimizer = ScipyPowell(
+    param_space=scipy_search_space,
+    n_iter=200,
+    experiment=objective,
+    random_state=42,
+)
+# [end:scipy_powell]
+
+
 # ============================================================================
 # Configuration Examples
 # ============================================================================

docs/source/api_reference/optimizers/index.rst

@@ -8,7 +8,7 @@ The :mod:`hyperactive.opt` module contains optimization algorithms for hyperpara
 All optimizers inherit from :class:`~hyperactive.base.BaseOptimizer` and share the same interface:
 the ``solve()`` method to run optimization, and configuration via the ``experiment`` and ``search_space`` parameters.
 
-Hyperactive provides optimizers from three backends:
+Hyperactive provides optimizers from four backends:
 
 .. list-table::
    :widths: 25 75
@@ -20,6 +20,8 @@ Hyperactive provides optimizers from three backends:
      - Native gradient-free optimization algorithms (21 optimizers)
    * - :doc:`optuna`
      - Interface to Optuna's samplers (8 optimizers)
+   * - :doc:`scipy`
+     - Scipy.optimize algorithms for continuous spaces (7 optimizers)
    * - :doc:`sklearn`
      - sklearn-compatible search interfaces (2 optimizers)
 
@@ -28,4 +30,5 @@ Hyperactive provides optimizers from three backends:
 
     gfo
     optuna
+    scipy
     sklearn

docs/source/api_reference/optimizers/scipy.rst

@@ -0,0 +1,37 @@
+.. _optimizers_scipy_ref:
+
+Scipy
+=====
+
+.. currentmodule:: hyperactive.opt
+
+The Scipy backend provides an interface to `scipy.optimize <https://docs.scipy.org/doc/scipy/reference/optimize.html>`_
+algorithms for continuous parameter optimization.
+
+.. note::
+
+    Scipy optimizers only support **continuous parameter spaces** (tuples).
+    For discrete or categorical parameters, use GFO or Optuna backends.
+
+Global Optimizers
+-----------------
+
+.. autosummary::
+    :toctree: ../auto_generated/
+    :template: class.rst
+
+    ScipyDifferentialEvolution
+    ScipyDualAnnealing
+    ScipyBasinhopping
+    ScipySHGO
+    ScipyDirect
+
+Local Optimizers
+----------------
+
+.. autosummary::
+    :toctree: ../auto_generated/
+    :template: class.rst
+
+    ScipyNelderMead
+    ScipyPowell

docs/source/examples.rst

@@ -18,6 +18,7 @@ on GitHub.
    examples/population_based
    examples/sequential_model_based
    examples/optuna_backend
+   examples/scipy_backend
    examples/sklearn_backend
    examples/integrations
    examples/other
@@ -61,6 +62,10 @@ Backend Examples
     Examples using Optuna's samplers including TPE, CMA-ES, NSGA-II/III,
     and Gaussian Process optimization.
 
+:ref:`examples_scipy_backend`
+    Examples using scipy.optimize algorithms including Differential Evolution,
+    Dual Annealing, Basin-hopping, SHGO, DIRECT, Nelder-Mead, and Powell.
+
 :ref:`examples_sklearn_backend`
     Scikit-learn compatible interfaces as drop-in replacements for
     GridSearchCV and RandomizedSearchCV.

docs/source/examples/scipy_backend.rst

@@ -0,0 +1,104 @@
+.. _examples_scipy_backend:
+
+=============
+Scipy Backend
+=============
+
+Hyperactive provides wrappers for scipy.optimize algorithms, enabling
+well-tested, production-grade optimization for continuous parameter spaces.
+
+.. note::
+
+   Scipy must be installed separately:
+
+   .. code-block:: bash
+
+       pip install scipy
+       # or
+       pip install hyperactive[all_extras]
+
+
+Available Optimizers
+--------------------
+
+The Scipy backend provides 7 optimizers divided into global and local methods.
+
+**Global Optimizers** (5 algorithms):
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Optimizer
+     - Description
+   * - ``ScipyDifferentialEvolution``
+     - Population-based global optimizer. Robust for multi-modal landscapes.
+   * - ``ScipyDualAnnealing``
+     - Combines classical simulated annealing with local search.
+   * - ``ScipyBasinhopping``
+     - Random perturbations with local minimization. Good for finding global minima.
+   * - ``ScipySHGO``
+     - Simplicial Homology Global Optimization. Finds multiple local minima.
+   * - ``ScipyDirect``
+     - Deterministic DIRECT algorithm. No random seed required.
+
+**Local Optimizers** (2 algorithms):
+
+.. list-table::
+   :header-rows: 1
+   :widths: 30 70
+
+   * - Optimizer
+     - Description
+   * - ``ScipyNelderMead``
+     - Simplex-based optimizer. Fast for smooth functions.
+   * - ``ScipyPowell``
+     - Conjugate direction method. Often faster than Nelder-Mead.
+
+
+Quick Example
+-------------
+
+Scipy optimizers require continuous parameter spaces defined as tuples:
+
+.. code-block:: python
+
+    from hyperactive.opt.scipy import ScipyDifferentialEvolution
+
+    # Define a continuous search space (tuples, not arrays)
+    param_space = {
+        "x": (-5.0, 5.0),
+        "y": (-5.0, 5.0),
+    }
+
+    def objective(params):
+        x, y = params["x"], params["y"]
+        return -(x**2 + y**2)  # Maximize (minimize negative)
+
+    optimizer = ScipyDifferentialEvolution(
+        param_space=param_space,
+        n_iter=100,
+        experiment=objective,
+        random_state=42,
+    )
+
+    best_params = optimizer.solve()
+    print(f"Best parameters: {best_params}")
+
+
+When to Use Scipy Backend
+-------------------------
+
+The Scipy backend is useful when:
+
+- **Continuous parameters only**: Your search space has no categorical or discrete values
+- **Production-grade algorithms**: You need well-tested, reliable implementations
+- **Specific scipy features**: You want scipy's differential evolution or simulated annealing
+- **Deterministic optimization**: Use ``ScipyDirect`` for reproducible results without random seeds
+
+
+See Also
+--------
+
+- :ref:`user_guide_optimizers_scipy` - Detailed guide with all optimizer examples
+- :ref:`optimizers_scipy_ref` - API reference for all Scipy optimizers

docs/source/user_guide/optimizers/index.rst

@@ -4,7 +4,7 @@
 Optimizers
 ==========
 
-Hyperactive provides 31 algorithms across 5 categories and 3 backends.
+Hyperactive provides 38 algorithms across 5 categories and 4 backends.
 Optimizers navigate the search space to find optimal parameters. Each implements a
 different strategy for balancing exploration (trying diverse regions) and exploitation
 (refining promising solutions). Local search methods like Hill Climbing work well for
@@ -20,10 +20,10 @@ Algorithm Landscape
 
    <div class="theme-aware-diagram">
       <img src="../../_static/diagrams/optimizer_taxonomy_light.svg"
-           alt="Hyperactive optimizer taxonomy showing 31 algorithms across GFO, Optuna, and sklearn backends"
+           alt="Hyperactive optimizer taxonomy showing 38 algorithms across GFO, Optuna, Scipy, and sklearn backends"
            class="only-light" />
       <img src="../../_static/diagrams/optimizer_taxonomy_dark.svg"
-           alt="Hyperactive optimizer taxonomy showing 31 algorithms across GFO, Optuna, and sklearn backends"
+           alt="Hyperactive optimizer taxonomy showing 38 algorithms across GFO, Optuna, Scipy, and sklearn backends"
            class="only-dark" />
    </div>
 
@@ -133,6 +133,17 @@ Algorithm Categories
 
       *TPEOptimizer, CmaEsOptimizer, GPOptimizer, NSGAIIOptimizer, and more*
 
+   .. grid-item-card:: Scipy Backend
+      :link: scipy
+      :link-type: doc
+      :class-card: sd-border-secondary
+
+      **7 algorithms**
+      ^^^
+      Scipy.optimize algorithms for continuous parameter spaces.
+
+      *DifferentialEvolution, DualAnnealing, Basinhopping, SHGO, Direct, NelderMead, Powell*
+
 ----
 
 Scenario Reference
@@ -163,8 +174,11 @@ Detailed recommendations based on problem characteristics:
      - ``GridSearch``
      - Exhaustive coverage when feasible
    * - Continuous parameters
-     - ``BayesianOptimizer``, ``CmaEsOptimizer``
+     - ``BayesianOptimizer``, ``CmaEsOptimizer``, ``ScipyDifferentialEvolution``
      - Designed for smooth, continuous spaces
+   * - Continuous only (scipy)
+     - ``ScipyDualAnnealing``, ``ScipyBasinhopping``, ``ScipyNelderMead``
+     - Production-grade scipy.optimize implementations
    * - Mixed parameter types
      - ``TPEOptimizer``, ``RandomSearch``
      - Handle categorical + continuous well
@@ -191,4 +205,5 @@ All optimizers share common parameters and configuration options.
    population_based
    sequential_model_based
    optuna
+   scipy
    configuration