Metadata-Version: 2.1
Name: fireflyalgorithm
Version: 0.4.6
Summary: Implementation of Firefly Algorithm in Python
Home-page: https://github.com/firefly-cpp/FireflyAlgorithm
License: MIT
Keywords: computational intelligence,firefly algorithm,swarm intelligence,optimization
Author: Iztok Fister Jr.
Author-email: iztok@iztok-jr-fister.eu
Requires-Python: >=3.9,<3.14
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Dist: numpy (>=1.26.1,<2.0.0)
Project-URL: Repository, https://github.com/firefly-cpp/FireflyAlgorithm
Description-Content-Type: text/markdown

<p align="center">
  <img width="200" src="https://raw.githubusercontent.com/firefly-cpp/FireflyAlgorithm/master/.github/imgs/firefly_logo.png">
</p>

<h1 align="center">
Firefly Algorithm --- Implementation of Firefly algorithm in Python
</h1>

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  <img alt="DOI" src="https://zenodo.org/badge/DOI/10.5281/zenodo.10430919.svg" href="https://doi.org/10.5281/zenodo.10430919">
</p>

<p align="center">
  <a href="#-about">📋 About</a> •
  <a href="#-installation">📦 Installation</a> •
  <a href="#-usage">🚀 Usage</a> •
  <a href="#-reference-papers">📚 Reference Papers</a> •
  <a href="#-cite-us">📄 Cite us</a> •
  <a href="#-license">🔑 License</a>
</p>

## 📋 About

This package implements a nature-inspired algorithm for optimization called Firefly Algorithm (FA) in Python programming language. 🌿🔍💻

## 📦 Installation

To install FireflyAlgorithm with pip, use:
```sh
pip install fireflyalgorithm
```
To install FireflyAlgorithm on Fedora, use:
```sh
dnf install python-fireflyalgorithm
```
To install FireflyAlgorithm on Arch Linux, please use an [AUR helper](https://wiki.archlinux.org/title/AUR_helpers):
```sh
$ yay -Syyu python-fireflyalgorithm
```
To install FireflyAlgorithm on Alpine Linux, use:
```sh
$ apk add py3-fireflyalgorithm
```

## 🚀 Usage

```python
from fireflyalgorithm import FireflyAlgorithm
from fireflyalgorithm.problems import sphere

FA = FireflyAlgorithm()
best = FA.run(function=sphere, dim=10, lb=-5, ub=5, max_evals=10000)

print(best)
```

### Test functions 📈

In the `fireflyalgorithm.problems` module, you can find the implementations of 33 popular optimization test problems.  Additionally, the module provides a utility function, `get_problem`, that allows you to retrieve a specific optimization problem function by providing its name as a string:

```python
from fireflyalgorithm.problems import get_problem

# same as from fireflyalgorithm.problems import rosenbrock
rosenbrock = get_problem('rosenbrock')
```

For more information about the implemented test functions, [click here](Problems.md).

### Command line interface 🖥️

The package also comes with a simple command line interface which allows you to evaluate the algorithm on several popular test functions. 🔬

```shell
firefly-algorithm -h
```

```text
usage: firefly-algorithm [-h] --problem PROBLEM -d DIMENSION -l LOWER -u UPPER -nfes MAX_EVALS [-r RUNS] [--pop-size POP_SIZE] [--alpha ALPHA] [--beta-min BETA_MIN] [--gamma GAMMA] [--seed SEED]

Evaluate the Firefly Algorithm on one or more test functions

options:
  -h, --help            show this help message and exit
  --problem PROBLEM     Test problem to evaluate
  -d DIMENSION, --dimension DIMENSION
                        Dimension of the problem
  -l LOWER, --lower LOWER
                        Lower bounds of the problem
  -u UPPER, --upper UPPER
                        Upper bounds of the problem
  -nfes MAX_EVALS, --max-evals MAX_EVALS
                        Max number of fitness function evaluations
  -r RUNS, --runs RUNS  Number of runs of the algorithm
  --pop-size POP_SIZE   Population size
  --alpha ALPHA         Randomness strength
  --beta-min BETA_MIN   Attractiveness constant
  --gamma GAMMA         Absorption coefficient
  --seed SEED           Seed for the random number generator
```

**Note:** The CLI script can also run as a python module (python -m fireflyalgorithm ...).


## 📚 Reference Papers

I. Fister Jr., X.-S. Yang, I. Fister, J. Brest, D. Fister. [A Brief Review of Nature-Inspired Algorithms for Optimization](http://www.iztok-jr-fister.eu/static/publications/21.pdf). Elektrotehniški vestnik, 80(3), 116-122, 2013.

I. Fister Jr., X.-S. Yang, I. Fister, J. Brest. [Memetic firefly algorithm for combinatorial optimization](http://www.iztok-jr-fister.eu/static/publications/44.pdf) in Bioinspired Optimization Methods and their Applications (BIOMA 2012), B. Filipic and J.Silc, Eds.
Jozef Stefan Institute, Ljubljana, Slovenia, 2012

I. Fister, I. Fister Jr., X.-S. Yang, J. Brest. [A comprehensive review of firefly algorithms](http://www.iztok-jr-fister.eu/static/publications/23.pdf). Swarm and Evolutionary Computation 13 (2013): 34-46.

## 📄 Cite us

Fister Jr., I., Pečnik, L., & Stupan, Ž. (2023). firefly-cpp/FireflyAlgorithm: 0.4.3 (0.4.3). Zenodo. [https://doi.org/10.5281/zenodo.10430919](https://doi.org/10.5281/zenodo.10430919)

## 🔑 License

This package is distributed under the MIT License. This license can be found online at <http://www.opensource.org/licenses/MIT>.

## Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!

