Add optimizers from PySwarms #639

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Expand Up		@@ -39,6 +39,7 @@ dependencies:
		- kaleido>=1.0 # dev, tests
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- pandas-stubs # dev, tests
		- types-cffi # dev, tests
		- types-openpyxl # dev, tests
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3 changes: 2 additions & 1 deletion .tools/envs/testenv-nevergrad.yml

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Expand Up		@@ -36,12 +36,13 @@ dependencies:
		- kaleido>=1.0 # dev, tests
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- pandas-stubs # dev, tests
		- types-cffi # dev, tests
		- types-openpyxl # dev, tests
		- types-jinja2 # dev, tests
		- sqlalchemy-stubs # dev, tests
	- sphinxcontrib-mermaid # dev, tests, docs
		- bayesian_optimization==1.4.0
		- nevergrad
	- sphinxcontrib-mermaid # dev, tests, docs
		- -e ../../

1 change: 1 addition & 0 deletions .tools/envs/testenv-numpy.yml

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Expand Up		@@ -37,6 +37,7 @@ dependencies:
		- kaleido>=1.0 # dev, tests
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- types-cffi # dev, tests
		- types-openpyxl # dev, tests
		- types-jinja2 # dev, tests
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1 change: 1 addition & 0 deletions .tools/envs/testenv-others.yml

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Expand Up		@@ -37,6 +37,7 @@ dependencies:
		- kaleido>=1.0 # dev, tests
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- pandas-stubs # dev, tests
		- types-cffi # dev, tests
		- types-openpyxl # dev, tests
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1 change: 1 addition & 0 deletions .tools/envs/testenv-pandas.yml

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Expand Up		@@ -37,6 +37,7 @@ dependencies:
		- kaleido>=1.0 # dev, tests
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- types-cffi # dev, tests
		- types-openpyxl # dev, tests
		- types-jinja2 # dev, tests
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3 changes: 2 additions & 1 deletion .tools/envs/testenv-plotly.yml

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Expand Up		@@ -36,11 +36,12 @@ dependencies:
		- fides==0.7.4 # dev, tests
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- pandas-stubs # dev, tests
		- types-cffi # dev, tests
		- types-openpyxl # dev, tests
		- types-jinja2 # dev, tests
		- sqlalchemy-stubs # dev, tests
	- sphinxcontrib-mermaid # dev, tests, docs
		- kaleido<0.3
	- sphinxcontrib-mermaid # dev, tests, docs
		- -e ../../

101 changes: 101 additions & 0 deletions docs/source/algorithms.md

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Expand Up		@@ -4885,6 +4885,107 @@ We wrap the pygad optimizer. To use it you need to have
		.. autoclass:: optimagic.optimizers.pygad_optimizer.Pygad
		```

	## PySwarms Optimizers

	optimagic supports the following continuous algorithms from the
	[PySwarms](https://pyswarms.readthedocs.io/en/latest/) library: (GlobalBestPSO,
	LocalBestPSO, GeneralOptimizerPSO). To use these optimizers, you need to have
	[the pyswarms package](https://github.com/ljvmiranda921/pyswarms) installed.
	(`pip install pyswarms`).

	```{eval-rst}
	.. dropdown:: pyswarms_global_best

	How to use this algorithm:

	.. code-block::

	import optimagic as om
	om.minimize(
	...,
	algorithm=om.algos.pyswarms_global_best(n_particles=50, ...)
	)

	or

	.. code-block::

	om.minimize(
	...,
	algorithm="pyswarms_global_best",
	algo_options={"n_particles": 50, ...}
	)

	Description and available options:

	.. autoclass:: optimagic.optimizers.pyswarms_optimizers.PySwarmsGlobalBestPSO
	:members:
	:inherited-members: Algorithm, object

	```

	```{eval-rst}
	.. dropdown:: pyswarms_local_best

	How to use this algorithm:

	.. code-block::

	import optimagic as om
	om.minimize(
	...,
	algorithm=om.algos.pyswarms_local_best(n_particles=50, k_neighbors=3, ...)
	)

	or

	.. code-block::

	om.minimize(
	...,
	algorithm="pyswarms_local_best",
	algo_options={"n_particles": 50, "k_neighbors": 3, ...}
	)

	Description and available options:

	.. autoclass:: optimagic.optimizers.pyswarms_optimizers.PySwarmsLocalBestPSO
	:members:
	:inherited-members: Algorithm, object

	```

	```{eval-rst}
	.. dropdown:: pyswarms_general

	How to use this algorithm:

	.. code-block::

	import optimagic as om
	om.minimize(
	...,
	algorithm=om.algos.pyswarms_general(n_particles=50, topology_type="star", ...)
	)

	or

	.. code-block::

	om.minimize(
	...,
	algorithm="pyswarms_general",
	algo_options={"n_particles": 50, "topology_type": "star", ...}
	)

	Description and available options:

	.. autoclass:: optimagic.optimizers.pyswarms_optimizers.PySwarmsGeneralPSO
	:members:
	:inherited-members: Algorithm, object

	```

		## References

		```{eval-rst}
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36 changes: 36 additions & 0 deletions docs/source/refs.bib

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Expand Up		@@ -1077,4 +1077,40 @@ @article{gad2023pygad
		publisher={Springer}
		}

	@INPROCEEDINGS{EberhartKennedy1995,
	author = {Eberhart, R. and Kennedy, J.},
	booktitle = {MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science},
	title = {A new optimizer using particle swarm theory},
	year = {1995},
	pages = {39-43},
	keywords = {Particle swarm optimization;Genetic algorithms;Testing;Acceleration;Particle tracking;Optimization methods;Artificial neural networks;Evolutionary computation;Performance evaluation;Statistics},
	doi = {10.1109/MHS.1995.494215}
	}

	@INPROCEEDINGS{Lane2008SpatialPSO,
	author={Lane, James and Engelbrecht, Andries and Gain, James},
	booktitle={2008 IEEE Swarm Intelligence Symposium},
	title={Particle swarm optimization with spatially meaningful neighbours},
	year={2008},
	volume={},
	number={},
	pages={1-8},
	keywords={Particle swarm optimization;Topology;Birds;Convergence;Computer science;USA Councils;Cities and towns;Africa;Cultural differences;Data structures;Delaunay Triangulation;Neighbour Topology;Particle Swarm Optimization;Heuristics},
	doi={10.1109/SIS.2008.4668281}
	}

	@article{Ni2013,
	author = {Ni, Qingjian and Deng, Jianming},
	title = {A New Logistic Dynamic Particle Swarm Optimization Algorithm Based on Random Topology},
	journal = {The Scientific World Journal},
	volume = {2013},
	number = {1},
	pages = {409167},
	doi = {https://doi.org/10.1155/2013/409167},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1155/2013/409167},
	eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1155/2013/409167},
	abstract = {Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.},
	year = {2013}
	}

		@Comment{jabref-meta: databaseType:bibtex;}

1 change: 1 addition & 0 deletions environment.yml

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Expand Up		@@ -51,6 +51,7 @@ dependencies:
		- pre-commit>=4 # dev
		- bayes_optim # dev, tests
		- gradient_free_optimizers # dev, tests
	- pyswarms # dev, tests
		- -e . # dev
		# type stubs
		- pandas-stubs # dev, tests
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2 changes: 2 additions & 0 deletions pyproject.toml

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Expand Up		@@ -384,6 +384,8 @@ module = [
		"iminuit",
		"nevergrad",
		"pygad",
	"pyswarms",
	"pyswarms.backend.topology",
		"yaml",
		"gradient_free_optimizers",
		"gradient_free_optimizers.optimizers.base_optimizer",
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