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Python micro framework for building nature-inspired algorithms.

Project description

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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 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.

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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.

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

  • pytest == 3.7.1
  • coverage == 4.4.2
  • coverage-space == 1.0.2
  • click == 6.0
  • numpy == 1.14.0
  • scipy == 1.0.0
  • xlsxwriter == 1.0.2
  • matplotlib == 2.2.2

List of development dependencies and requirements can be found in the installation section of NiaPy documentation.

Installation

Install NiaPy with pip:

$ pip install NiaPy

Install NiaPy with conda:

$ conda install -c niaorg 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

Open Source Helpers

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

2.0.0rc3 (Nov 30, 2018)

  • added moth flame optimizer
  • added new examples
  • documentation updates
  • PSO and BBFWA algorithms fixes
  • stopping conditions fixes
  • added new test cases
  • added multiple seed option
  • various bugfixes

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

1.0.2 (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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