niapy 0.1.3a1

Python micro framework for building nature-inspired algorithms.

About

Nature-inspired algorithms are a very popular tool for solving optimization problems. Numerous variants of nature-inspired algorithms have been developed 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.

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.

The micro framework features following algorithms:

• basic:

• Artificial bee colony algorithm (see example)

• Bat algorithm (see example)

• Cuckoo Search algorithm (see example)

• Differential evolution algorithm (see example)

• Firefly algorithm (see example)

• Flower pollination algorithm (see example)

• Genetic algorithm (see example)

• Grey wolf optimizer (see example)

• Particle swarm optimization (see example)

• modified:

• Hybrid bat algorithm (see example)

• Self-adaptive differential evolution algorithm (see example)

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

Installation

Install NiaPy with pip (will be available soon):

$ pip install NiaPy

or directly from the source code:

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

Usage

After installation, the package can imported:

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

For more usage examples please look at examples folder.

Contributing

We encourage you to contribute to NiaPy! Please check out the Contributing to NiaPy guide 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.

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

0.1.3a1

• fixes image issue in PyPI project description

0.1.2a4 - fixes problem with build scripts

0.1.2a3

• fixes PyPI project description

• alpha3 version