Metadata-Version: 2.1
Name: swarmlib
Version: 0.3.2
Summary: Implementation and visualization of different swarm optimization algorithms.
Home-page: https://github.com/HaaLeo/swarmlib
Author: Leo Hanisch
License: BSD 3-Clause License
Project-URL: Issue Tracker, https://github.com/HaaLeo/swarmlib/issues
Project-URL: Changelog, https://github.com/HaaLeo/swarmlib/blob/master/CHANGELOG.md#changelog
Keywords: swarm,swarmlib,ant,colony,optimization,optimisation,traveling,salesman,problem,TSP,tsp,ACO,aco,TSPLIB95,tsplib95networkx,visualization,matplotlib,firefly,fireflies,algorithm
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Education
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: tsplib95 (<1.0.0,>=0.3.2)
Requires-Dist: matplotlib (<4.0.0,>=3.0.2)
Requires-Dist: networkx (==2.1)
Requires-Dist: numpy (<2.0.0,>=1.15.4)

# swarmlib

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## Description

This repository implements several optimization algorithms.

## Installation

You can install the package with `pip` from [pypi](https://pypi.org/project/swarmlib):

```
pip3 install swarmlib

swarm --version
```

## Usage

To print all available algorithms:

```
swarm --help
```

## Ant Colony Optimization

This repository includes an ant colony optimization algorithm for the traveling salesman problem (TSP) like Marco Dorigo, Mauro Birattari, and Thomas Stuetzle introduced in the [IEEE Computational Intelligence Magazine](https://ieeexplore.ieee.org/document/4129846) in November 2006 (DOI: 10.1109/MCI.2006.329691).  
The implementation was part of the course [Natural computing for learning and optimisation](https://is.cuni.cz/studium/eng/predmety/index.php?do=predmet&kod=NPFL107) at Charles University Prague in winter 2018/2019.

### Features

Enables to apply the ant colony optimization algorithm to a TSP using a [TSPLIB95](http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/) file and plots the result.

![ACO Sample](https://raw.githubusercontent.com/HaaLeo/swarmlib/master/doc/ACO_Sample.png)

The algorithm solves the TSP and plots the result all _n_ iterations.  
The nodes are plot according to their coordinates read from the TSPLIB95 file. The _widths_ of the edges indicate the _amount of pheromone_ that is associated with this edge. If an edge is _blue_, it is part of the _best found path_.

To print all available options execute:

```
swarm ants -h
```

### API

In addition to the client you can also use the API:

```python
from swarmlib.aco4tsp.aco_problem import ACOProblem

problem = ACOProblem('/path/to/my/tsp-file.tsp', 10)
if problem.solve():
    problem.show_result()
```

## Firefly Algorithm

This repository includes the firefly algorithm like Xin-She Yang introduced in his paper [Firefly Algorithms for Multimodal Optimization](https://link.springer.com/chapter/10.1007%2F978-3-642-04944-6_14) in 2009 (DOI: 10.1007/978-3-642-04944-6_14).  
The implementation was part of the course [Natural computing for learning and optimisation](https://is.cuni.cz/studium/eng/predmety/index.php?do=predmet&kod=NPFL107) at Charles University Prague in winter 2018/2019.

### Features

Enables to apply the firefly algorithm to one of the provided 2D functions. The algorithm tries to find the global minimum of the selected function.  

Currently two functions can be selected:
* [ackley](https://www.sfu.ca/~ssurjano/ackley.html)
* [michalewicz](https://www.sfu.ca/~ssurjano/michal.html)

![firefly algorithm](https://raw.githubusercontent.com/HaaLeo/swarmlib/master/doc/fireflies.gif)

To print all available options execute:

```
swarm fireflies -h
```

### API

In addition to the client you can also use the API:

```python
from swarmlib.fireflyalgorithm.firefly_problem import FireflyProblem
from swarmlib.fireflyalgorithm.functions import FUNCTIONS

problem = FireflyProblem(FUNCTIONS['michalewicz'], 14)
problem.solve()
```

## Contribution

If you found a bug or are missing a feature do not hesitate to [file an issue](https://github.com/HaaLeo/swarmlib/issues/new/choose).  
Pull Requests are welcome!

## Support
When you like this package make sure to [star the repository](https://github.com/HaaLeo/swarmlib/stargazers). I am always looking for new ideas and feedback.  
In addition, it is possible to [donate via paypal](https://www.paypal.me/LeoHanisch/3eur).


