Metadata-Version: 2.1
Name: NiaPy
Version: 1.0.2
Summary: Python micro framework for building nature-inspired algorithms.
Home-page: https://github.com/NiaOrg/NiaPy
Author: NiaOrg
Author-email: niapy.organization@gmail.com
License: MIT
Description-Content-Type: UNKNOWN
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Requires-Dist: numpy (~=1.14.0)
Requires-Dist: click (~=6.0)
Requires-Dist: scipy (~=1.0.0)
Requires-Dist: xlsxwriter (~=1.0.2)

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About
=====

Nature-inspired algorithms are a very popular tool for solving
optimization problems. Numerous variants of `nature-inspired algorithms
have been developed <https://arxiv.org/abs/1307.4186>`__ since the
beginning of their era. To prove their versatility, those were tested in
various domains on various applications, especially when they are
hybridized, modified or adapted. However, implementation of
nature-inspired algorithms is sometimes a difficult, complex and tedious
task. In order to break this wall, NiaPy is intended for simple and
quick use, without spending time for implementing algorithms from
scratch.


.. image:: http://c1.staticflickr.com/5/4757/26625486258_41ea6d95e0.jpg
    :align: center

Mission
-------

| Our mission is to build a collection of nature-inspired algorithms and
  create a simple interface for managing the optimization process.
| NiaPy will offer:

-  numerous benchmark functions implementations,
-  use of various nature-inspired algorithms without struggle and effort
   with a simple interface,
-  easy comparison between nature-inspired algorithms and
-  export of results in various formats (LaTeX, JSON, Excel).

Overview
========

Python micro framework for building nature-inspired algorithms. Official documentation is available `here <http://niapy.readthedocs.io/en/1.0.0>`_.

The micro framework features following algorithms:

-  basic:
    -  Artificial bee colony algorithm
    -  Bat algorithm
    -  Differential evolution algorithm
    -  Firefly algorithm
    -  Flower pollination algorithm
    -  Genetic algorithm
    -  Grey wolf optimizer
    -  Particle swarm optimization
-  modified:
    -  Hybrid bat algorithm
    -  Self-adaptive differential evolution algorithm

The following benchmark functions are included in NiaPy:

-  Ackley
-  Alpine
    -  Alpine1
    -  Alpine2
-  Chung Reynolds
-  Csendes
-  Griewank
-  Happy cat
-  Pintér
-  Qing
-  Quintic
-  Rastrigin
-  Ridge
-  Rosenbrock
-  Salomon
-  Schumer Steiglitz
-  Schwefel
    -  Schwefel 2.21
    -  Schwefel 2.22
-  Sphere
-  Step
    -  Step2
    -  Step3
-  Stepint
-  Styblinski-Tang
-  Sum Squares
-  Whitley

Setup
=====

Requirements
------------

-  Python 3.6+ (backward compatibility with 2.7.14)
-  Pip

Dependencies
~~~~~~~~~~~~

-  click == *
-  numpy == 1.14.0
-  scipy == 1.0.0
-  xlsxwriter == 1.0.2

List of development dependencies and requirements can be found in the `installation section of NiaPy documentation <http://niapy.readthedocs.io/en/stable/installation.html>`_.

Installation
------------

Install NiaPy with pip:

.. code:: sh

    $ pip install NiaPy

or directly from the source code:

.. code:: sh

    $ git clone https://github.com/NiaOrg/NiaPy.git
    $ cd NiaPy
    $ python setup.py install

Usage
=====

After installation, the package can imported:

.. code:: sh

    $ python
    >>> import NiaPy
    >>> NiaPy.__version__

For more usage examples please look at **examples** folder.

Contributing
------------

|Open Source Helpers|

We encourage you to contribute to NiaPy! Please check out the
`Contributing to NiaPy guide <CONTRIBUTING.md>`__ for guidelines about
how to proceed.

Everyone interacting in NiaPy's codebases, issue trackers, chat rooms
and mailing lists is expected to follow the NiaPy `code of
conduct <CODE_OF_CONDUCT.md>`__.

Licence
-------

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!



Revision History
================

1.0.2 (Oct 24, 2018)
--------------------
- fix Bat and Hybrid Bat algorithms

1.0.1 (Mar 21, 2018)
--------------------
This release reflects the changes from Journal of Open Source Software (JOSS) review:
- Better API Documentation
- Clarification of set-up requirements in README
- Improved paper

1.0.0 (Feb 28, 2018)
--------------------
- stable release 1.0.0

1.0.0rc2 (Feb 28, 2018)
-----------------------
- fix PyPI build

1.0.0rc1 (Feb 28, 2018)
-----------------------
- version 1.0.0 release candidate 1
- added 10 algorithms
- added 26 benchmark functions
- added Runner utility with export functionality


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   :target: https://travis-ci.org/NiaOrg/NiaPy
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   :target: https://coveralls.io/r/NiaOrg/NiaPy
.. |Scrutinizer Code Quality| image:: https://img.shields.io/scrutinizer/g/NiaOrg/NiaPy.svg
   :target: https://scrutinizer-ci.com/g/NiaOrg/NiaPy/?branch=master
.. |PyPI Version| image:: https://img.shields.io/pypi/v/NiaPy.svg
   :target: https://pypi.python.org/pypi/NiaPy
.. |Documentation Status| image:: https://readthedocs.org/projects/niapy/badge/?version=latest
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   :target: https://github.com/NiaOrg/NiaPy/blob/master/LICENSE
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