Metadata-Version: 2.1
Name: NiaPy
Version: 2.0.0rc2
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: pytest (~=3.7.1)
Requires-Dist: coverage (~=4.4.2)
Requires-Dist: coverage-space (~=1.0.2)
Requires-Dist: numpy (~=1.14.0)
Requires-Dist: enum34 (~=1.1.6)
Requires-Dist: click (~=6.0)
Requires-Dist: scipy (~=1.0.0)
Requires-Dist: xlsxwriter (~=1.0.2)
Requires-Dist: matplotlib (~=2.2.2)

|Unix Build Status|
|Windows Build status|
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|PyPI Version|
|Documentation Status|
|GitHub license|

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
    -  Camel algorithm
    -  Differential evolution algorithm
    -  Evolution Strategy
    -  Firefly algorithm
    -  Fireworks algorithm
    -  Flower pollination algorithm
    -  Genetic algorithm
    -  Glowworm swarm optimization
    -  Grey wolf optimizer
    -  Harmony Search algorithm
    -  Krill herd algorithm
    -  Monkey king evolution
    -  Multiple trajectory search
    -  Particle swarm optimization
    -  Sine cosine algorithm
-  modified:
    -  Hybrid bat algorithm
    -  Self-adaptive differential evolution algorithm
    -  Dynamic population size self-adaptive differential evolution algorithm
-  other:
    -  Anarchic society optimization algorithm
    -  Hill climbing algorithm
    -  Multiple trajectory search
    -  Nelder mead method or downhill simplex method or amoeba method
    -  Simulated annealing algorithm

The following benchmark functions are included in NiaPy:

-  Ackley
-  Alpine
    -  Alpine1
    -  Alpine2
-  Bent Cigar
-  Chung Reynolds
-  Csendes
-  Discus
-  Dixon-Price
-  Elliptic
-  Griewank
-  Happy cat
-  HGBat
-  Katsuura
-  Levy
-  Michalewicz
-  Perm
-  Pintér
-  Powell
-  Qing
-  Quintic
-  Rastrigin
-  Ridge
-  Rosenbrock
-  Salomon
-  Schumer Steiglitz
-  Schwefel
    -  Schwefel 2.21
    -  Schwefel 2.22
-  Sphere
    -  Sphere2 -> Sphere with different powers
    -  Sphere3 -> Rotated hyper-ellipsoid
-  Step
    -  Step2
    -  Step3
-  Stepint
-  Styblinski-Tang
-  Sum Squares
-  Trid
-  Weierstrass
-  Whitley
-  Zakharov

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
-  matplotlib == *

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
================

2.0.0rc2 (Aug 30, 2018)
-----------------------

- fix PyPI build

2.0.0rc1 (Aug 30, 2018)
-----------------------
Changes included in release:

- Added algorithms:
    - basic:
        - Camel algorithm
        - Evolution Strategy
        - Fireworks algorithm
        - Glowworm swarm optimization
        - Harmony search algorithm
        - Krill Herd Algorithm
        - Monkey King Evolution
        - Multiple trajectory search
        - Sine Cosine Algorithm
    - modified:
        - Dynamic population size self-adaptive differential evolution algorithm
    - other:
        - Anarchic society optimization algorithm
        - Hill climbing algorithm
        - Multiple trajectory search
        - Nelder mead method or downhill simplex method or amoeba method
        - Simulated annealing algorithm

- Added benchmarks functions:
    - Discus
    - Dixon-Price
    - Elliptic
    - HGBat
    - Katsuura
    - Levy
    - Michalewicz
    - Perm
    - Powell
    - Sphere2 -> Sphere with different powers
    - Sphere3 -> Rotated hyper-ellipsoid
    - Trid
    - Weierstrass
    - Zakharov

- **breaking changes** in algorithms structure
- various bugfixes

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


.. |Unix Build Status| image:: https://img.shields.io/travis/NiaOrg/NiaPy/master.svg
   :target: https://travis-ci.org/NiaOrg/NiaPy
.. |Windows Build status| image:: https://ci.appveyor.com/api/projects/status/l5c0rp04mp04mbtq?svg=true
   :target: https://ci.appveyor.com/project/GregaVrbancic/niapy
.. |Coverage Status| image:: https://img.shields.io/coveralls/NiaOrg/NiaPy/master.svg
   :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
   :target: http://niapy.readthedocs.io/en/latest/?badge=latest
.. |Average time to resolve an issue| image:: http://isitmaintained.com/badge/resolution/NiaOrg/NiaPy.svg
   :target: http://isitmaintained.com/project/NiaOrg/NiaPy
.. |Percentage of issues still open| image:: http://isitmaintained.com/badge/open/NiaOrg/NiaPy.svg
   :target: http://isitmaintained.com/project/NiaOrg/NiaPy
.. |GitHub license| image:: https://img.shields.io/github/license/NiaOrg/NiaPy.svg
   :target: https://github.com/NiaOrg/NiaPy/blob/master/LICENSE
.. |Open Source Helpers| image:: https://www.codetriage.com/niaorg/niapy/badges/users.svg
   :target: https://www.codetriage.com/niaorg/niapy



