NOTICE: I am not actively maintaining this repository anymore. My research interests have changed in the past few years. I highly recommend checking out scikit-opt for metaheuristic methods including PSO.

PySwarms is an extensible research toolkit for particle swarm optimization (PSO) in Python.

It is intended for swarm intelligence researchers, practitioners, and students who prefer a high-level declarative interface for implementing PSO in their problems. PySwarms enables basic optimization with PSO and interaction with swarm optimizations. Check out more features below!

• Free software: MIT license
• Documentation: https://pyswarms.readthedocs.io.
• Python versions: 3.5 and above

Features

• High-level module for Particle Swarm Optimization. For a list of all optimizers, check this link.
• Built-in objective functions to test optimization algorithms.
• Plotting environment for cost histories and particle movement.
• Hyperparameter search tools to optimize swarm behaviour.
• (For Devs and Researchers): Highly-extensible API for implementing your own techniques.

Installation

To install PySwarms, run this command in your terminal:

shell $ pip install pyswarms

This is the preferred method to install PySwarms, as it will always install the most recent stable release.

In case you want to install the bleeding-edge version, clone this repo:

shell $ git clone -b development https://github.com/ljvmiranda921/pyswarms.git and then run

shell $ cd pyswarms $ python setup.py install

To install PySwarms on Fedora, use:

sh $ dnf install python3-pyswarms

Running in a Vagrant Box

To run PySwarms in a Vagrant Box, install Vagrant by going to https://www.vagrantup.com/downloads.html and downloading the proper packaged from the Hashicorp website.

Afterward, run the following command in the project directory:

shell $ vagrant provision $ vagrant up $ vagrant ssh Now you're ready to develop your contributions in a premade virtual environment.

Basic Usage

PySwarms provides a high-level implementation of various particle swarm optimization algorithms. Thus, it aims to be user-friendly and customizable. In addition, supporting modules can be used to help you in your optimization problem.

Optimizing a sphere function

You can import PySwarms as any other Python module,

python import pyswarms as ps

Suppose we want to find the minima of f(x) = x^2 using global best PSO, simply import the built-in sphere function, pyswarms.utils.functions.sphere(), and the necessary optimizer:

```python import pyswarms as ps from pyswarms.utils.functions import single_obj as fx

Set-up hyperparameters

options = {'c1': 0.5, 'c2': 0.3, 'w':0.9}

Call instance of PSO

optimizer = ps.single.GlobalBestPSO(n_particles=10, dimensions=2, options=options)

Perform optimization

bestcost, bestpos = optimizer.optimize(fx.sphere, iters=100) ```

This will run the optimizer for 100 iterations, then returns the best cost and best position found by the swarm. In addition, you can also access various histories by calling on properties of the class:

```python

Obtain the cost history

optimizer.cost_history

Obtain the position history

optimizer.pos_history

Obtain the velocity history

optimizer.velocity_history ```

At the same time, you can also obtain the mean personal best and mean neighbor history for local best PSO implementations. Simply call optimizer.mean_pbest_history and optimizer.mean_neighbor_history respectively.

Hyperparameter search tools

PySwarms implements a grid search and random search technique to find the best parameters for your optimizer. Setting them up is easy. In this example, let's try using pyswarms.utils.search.RandomSearch to find the optimal parameters for LocalBestPSO optimizer.

Here, we input a range, enclosed in tuples, to define the space in which the parameters will be found. Thus, (1,5) pertains to a range from 1 to 5.

```python import numpy as np import pyswarms as ps from pyswarms.utils.search import RandomSearch from pyswarms.utils.functions import single_obj as fx

Set-up choices for the parameters

options = { 'c1': (1,5), 'c2': (6,10), 'w': (2,5), 'k': (11, 15), 'p': 1 }

Create a RandomSearch object

nselectioniters is the number of iterations to run the searcher

iters is the number of iterations to run the optimizer

g = RandomSearch(ps.single.LocalBestPSO, nparticles=40, dimensions=20, options=options, objectivefunc=fx.sphere, iters=10, nselectioniters=100)

bestscore, bestoptions = g.search() ```

This then returns the best score found during optimization, and the hyperparameter options that enable it.

```s

bestscore 1.41978545901 bestoptions['c1'] 1.543556887693 best_options['c2'] 9.504769054771 ```

Swarm visualization

It is also possible to plot optimizer performance for the sake of formatting. The plotters module is built on top of matplotlib, making it highly-customizable.

```python import pyswarms as ps from pyswarms.utils.functions import singleobj as fx from pyswarms.utils.plotters import plotcosthistory, plotcontour, plot_surface import matplotlib.pyplot as plt

Set-up optimizer

options = {'c1':0.5, 'c2':0.3, 'w':0.9} optimizer = ps.single.GlobalBestPSO(n_particles=50, dimensions=2, options=options) optimizer.optimize(fx.sphere, iters=100)

Plot the cost

plotcosthistory(optimizer.cost_history) plt.show() ```

We can also plot the animation...

```python from pyswarms.utils.plotters.formatters import Mesher, Designer

Plot the sphere function's mesh for better plots

m = Mesher(func=fx.sphere, limits=[(-1,1), (-1,1)])

Adjust figure limits

d = Designer(limits=[(-1,1), (-1,1), (-0.1,1)], label=['x-axis', 'y-axis', 'z-axis']) ```

In 2D,

python plot_contour(pos_history=optimizer.pos_history, mesher=m, designer=d, mark=(0,0))

Or in 3D!

python pos_history_3d = m.compute_history_3d(optimizer.pos_history) # preprocessing animation3d = plot_surface(pos_history=pos_history_3d, mesher=m, designer=d, mark=(0,0,0))

Contributing

PySwarms is currently maintained by a small yet dedicated team: - Lester James V. Miranda (@ljvmiranda921) - Siobhán K. Cronin (@SioKCronin) - Aaron Moser (@whzup) - Steven Beardwell (@stevenbw)

And we would appreciate it if you can lend a hand with the following:

• Find bugs and fix them
• Update documentation in docstrings
• Implement new optimizers to our collection
• Make utility functions more robust.

We would also like to acknowledge all our contributors, past and present, for making this project successful!

If you wish to contribute, check out our contributing guide. Moreover, you can also see the list of features that need some help in our Issues page.

Most importantly, first-time contributors are welcome to join! I try my best to help you get started and enable you to make your first Pull Request! Let's learn from each other!

Credits

This project was inspired by the pyswarm module that performs PSO with constrained support. The package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Cite us

Are you using PySwarms in your project or research? Please cite us!

• Miranda L.J., (2018). PySwarms: a research toolkit for Particle Swarm Optimization in Python. Journal of Open Source Software, 3(21), 433, https://doi.org/10.21105/joss.00433

bibtex @article{pyswarmsJOSS2018, author = {Lester James V. Miranda}, title = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython", journal = {Journal of Open Source Software}, year = {2018}, volume = {3}, issue = {21}, doi = {10.21105/joss.00433}, url = {https://doi.org/10.21105/joss.00433} }

Projects citing PySwarms

Not on the list? Ping us in the Issue Tracker!

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• Vishwakarma, Gaurav. Machine Learning Model Selection for Predicting Properties of High Refractive Index Polymers Dissertation. State University of New York at Buffalo, 2018.
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• Lin, Y.H., He, D., Wang, Y. Lee, L.J. "Last-mile Delivery: Optimal Locker locatuion under Multinomial Logit Choice Model" https://arxiv.org/abs/2002.10153
• Park J., Kim S., Lee, J. "Supplemental Material for Ultimate Light trapping in free-form plasmonic waveguide" KAIST, University of Cambridge, and Cornell University http://www.jlab.or.kr/documents/publications/2019PRApplied_SI.pdf
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• Shang, Z. "Performance Evaluation of the Control Plane in OpenFlow Networks," Freie Universitat Berlin (2020)
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• Menke, Tim, Hase, Florian, et al. "Automated discovery of superconducting circuits and its application to 4-local coupler design," arxiv preprint: https://arxiv.org/abs/1912.03322

Others

Like it? Love it? Leave us a star on Github to show your appreciation!

Contributors

Thanks goes to these wonderful people (emoji key):

Aaron 🚧 💻 📖 ⚠️ 🤔 👀	Carl-K 💻 ⚠️	Siobhán K Cronin 💻 🚧 🤔	Andrew Jarcho ⚠️ 💻	Mamady 💻	Jay Speidell 💻	Eric 🐛 💻
CPapadim 🐛 💻	JiangHui 💻	Jericho Arcelao 💻	James D. Bohrman 💻	bradahoward 💻	ThomasCES 💻	Daniel Correia 🐛 💻
fluencer 💡 📖	miguelcocruz 📖 💡	Steven Beardwell 💻 🚧 📖 🤔	Nathaniel Ngo 📖	Aneal Sharma 📖	Chris McClure 📖 💡	Christopher Angell 📖
Kutim 🐛	Jake Souter 🐛 💻	Ian Zhang 📖 💡	Zach 📖	Michel Lavoie 🐛	ewekam 📖	Ivyna Santino 📖 💡
Muhammad Yasirroni 📖	Christian Kastner 📖 📦	Nishant Rodrigues 💻	msat59 💻 🐛	Diego 📖	Shaad Alaka 📖	Krzysztof Błażewicz 🐛
Jorge Castillo 📖	Philipp Danner 💻	Nikhil Sethi 💻 📖	firefly-cpp 📖

This project follows the all-contributors specification. Contributions of any kind welcome!