Add wrapper for Nevergrad PSO optimizer

.tools/envs/testenv-linux.yml

@@ -28,6 +28,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - pip:  # dev, tests, docs
+      - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests

.tools/envs/testenv-numpy.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - pip:  # dev, tests, docs
+      - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests

.tools/envs/testenv-others.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - pip:  # dev, tests, docs
+      - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests

.tools/envs/testenv-pandas.yml

@@ -26,6 +26,7 @@ dependencies:
   - jinja2  # dev, tests
   - annotated-types  # dev, tests
   - pip:  # dev, tests, docs
+      - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests

docs/source/algorithms.md

@@ -3936,6 +3936,61 @@ addition to optimagic when using an NLOPT algorithm. To install nlopt run
       10 * (number of parameters + 1).
 ```
 
+(nevergrad-algorithms)=
+
+## Nevergrad Optimizers
+
+optimagic supports some algorithms from the
+[Nevergrad](https://facebookresearch.github.io/nevergrad/index.html) library. To use
+these optimizers, you need to have
+[the nevergrad package](https://github.com/facebookresearch/nevergrad) installed.
+(`pip install nevergrad`).
+
+```{eval-rst}
+.. dropdown:: nevergrad_pso
+
+    .. code-block::
+
+        "nevergrad_pso"
+
+    Minimize a scalar function using the Particle Swarm Optimization (PSO) algorithm.
+
+    The Particle Swarm Optimization algorithm was originally proposed by
+    :cite:`Kennedy1995`. The implementation in Nevergrad is based on
+    :cite:`Zambrano2013`.
+
+    Particle Swarm Optimization (PSO) solves a problem by having a population of
+    candidate solutions, here dubbed particles, and moving these particles around in the
+    search-space according to simple mathematical formulae over the particle's position
+    and velocity. Each particle's movement is influenced by its local best known
+    position (termed "cognitive" component), but is also guided toward the best known
+    positions (termed "social" component) in the search-space, which are updated as
+    better positions are found by other particles. This is expected to move the swarm
+    toward the best solutions.
+
+    - **transform** (str): The transform to use to map from PSO optimization space to
+      R-space.
+    - **population_size** (int): Population size of the particle swarm.
+    - **n_cores** (int): Number of cores to use.
+    - **stopping.maxfun** (int): Maximum number of function evaluations.
+    - **inertia** (float): Inertia weight. Denoted by :math:`\omega`.
+      Default is 0.7213475204444817. To prevent divergence, the value must be smaller
+      than 1. It controls the influence of the particle's previous velocity on its
+      movement.
+    - **cognitive** (float): Cognitive coefficient. Denoted by :math:`\phi_p`.
+      Default is 1.1931471805599454. Typical values range from 1.0 to 3.0. It controls
+      the influence of its own best known position on the particle's movement.
+    - **social** (float): Social coefficient. Denoted by :math:`\phi_g`.
+      Default is 1.1931471805599454. Typical values range from 1.0 to 3.0. It controls
+      the influence of the swarm's best known position on the particle's movement.
+    - **quasi_opp_init** (bool): Whether to use quasi-opposition initialization.
+      Default is False.
+    - **speed_quasi_opp_init** (bool): Whether to use quasi-opposition initialization
+      for speed. Default is False.
+    - **special_speed_quasi_opp_init** (bool): Whether to use special quasi-opposition
+      initialization for speed. Default is False.
+```
+
 ## References
 
 ```{eval-rst}

docs/source/refs.bib

@@ -893,4 +893,27 @@ @book{Conn2009
 URL = {https://epubs.siam.org/doi/abs/10.1137/1.9780898718768},
 }
 
+
+@InProceedings{Kennedy1995,
+  author={Kennedy, J. and Eberhart, R.},
+  booktitle={Proceedings of ICNN'95 - International Conference on Neural Networks}, 
+  title={Particle swarm optimization}, 
+  year={1995},
+  volume={4},
+  pages={1942-1948 vol.4},
+  keywords={Particle swarm optimization;Birds;Educational institutions;Marine animals;Testing;Humans;Genetic algorithms;Optimization methods;Artificial neural networks;Performance evaluation},
+  doi={10.1109/ICNN.1995.488968},
+}
+
+
+@InProceedings{Zambrano2013,
+  author = {Zambrano-Bigiarini, Mauricio and Clerc, Maurice and Rojas, Rodrigo},
+  booktitle = {2013 IEEE Congress on Evolutionary Computation}, 
+  title = {Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements}, 
+  year = {2013},
+  pages = {2337-2344},
+  keywords = {Optimization;Standards;Benchmark testing;Topology;Algorithm design and analysis;Convergence;Equations;particle swarm optimization;SPSO-2011;CEC-2013;random topology;rotational invariance;benchmark testing;evolutionary computation;optimization},
+  doi = {10.1109/CEC.2013.6557848},
+}
+
 @Comment{jabref-meta: databaseType:bibtex;}

environment.yml

@@ -38,6 +38,7 @@ dependencies:
   - furo  # dev, docs
   - annotated-types  # dev, tests
   - pip:  # dev, tests, docs
+      - nevergrad  # dev, tests
       - DFO-LS>=1.5.3  # dev, tests
       - Py-BOBYQA  # dev, tests
       - fides==0.7.4  # dev, tests

pyproject.toml

@@ -378,5 +378,6 @@ module = [
     "optimagic._version",
     "annotated_types",
     "pdbp",
+    "nevergrad",
   ]
 ignore_missing_imports = true