NiaPy 2.0.0rc14
Python micro framework for building nature-inspired algorithms.
License: MIT

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|DOI| |image1|

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.

-  **Free software:** MIT license
-  **Documentation:** https://niapy.readthedocs.io/en/stable/
-  **Python versions:** 3.6.x, 3.7.x or 3.8.x (backward compatibility
   with 2.7.x)
-  **Dependencies:**
   `click <CONTRIBUTING.md#development-dependencies>`__

Mission
=======

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

-  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 such as Pandas DataFrame, JSON
   or even Excel (only when using Python >= 3.6).

Installation
============

To install NiaPy, run this command in your terminal:

.. code:: sh

   $ pip install NiaPy

This is the preferred method to install NiaPy, as it will always install
the most recent stable release.

To install NiaPy with conda, use:

.. code:: sh

   conda install -c niaorg niapy

To install NiaPy on Fedora, use:

.. code:: sh

   yum install python3-niapy

In case you want to install directly from the source code, use:

.. code:: sh

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

Usage
=====

After installation, you can import NiaPy as any other Python module:

.. code:: sh

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

Let’s go through a basic and advanced example.

Basic Example
-------------

Let’s say, we want to try out Gray Wolf Optimizer algorithm against
Pintér benchmark function. Firstly, we have to create new file, with
name, for example *basic_example.py*. Then we have to import chosen
algorithm from NiaPy, so we can use it. Afterwards we initialize
GreyWolfOptimizer class instance and run the algorithm. Given bellow is
complete source code of basic example.

.. code:: sh

   from NiaPy.algorithms.basic import GreyWolfOptimizer
   from NiaPy.task import StoppingTask

   # we will run 10 repetitions of Grey Wolf Optimizer against Pinter benchmark function
   for i in range(10):
       task = StoppingTask(D=10, nFES=1000, benchmark='pinter')
       algorithm = GreyWolfOptimizer(NP=20)
       best = algorithm.run(task)
       print(best[-1])

Given example can be run with *python basic_example.py* command and
should give you similar output as following:

.. code:: sh

   0.27046073106003377
   50.89301186976975
   1.089147452727528
   1.18418058254198
   102.46876441081712
   0.11237241605812048
   1.8869331711450696
   0.04861881403346098
   2.5748611081742325
   135.6754069530421

Advanced Example
----------------

In this example we will show you how to implement your own benchmark
function and use it with any of implemented algorithms. First let’s
create new file named *advanced_example.py*. As in the previous examples
we wil import algorithm we want to use from NiaPy module.

For our custom benchmark function, we have to create new class. Let’s
name it MyBenchmark. In the initialization method of MyBenchmark class
we have to set Lower and Upper bounds of the function. Afterwards we
have to implement a function which returns evaluation function which
takes two parameters *D* (as dimension of problem) and *sol* (as
solution of problem). Now we should have something similar as is shown
in code snippet bellow.

.. code:: sh

   from NiaPy.task import StoppingTask, OptimizationType
   from NiaPy.benchmarks import Benchmark
   from NiaPy.algorithms.basic import ParticleSwarmAlgorithm

   # our custom benchmark class
   class MyBenchmark(Benchmark):
       def __init__(self):
           Benchmark.__init__(self, -10, 10)

       def function(self):
           def evaluate(D, sol):
               val = 0.0
               for i in range(D): val += sol[i] ** 2
               return val
           return evaluate

Now, all we have to do is to initialize our algorithm as in previous
examples and pass as benchmark parameter, instance of our MyBenchmark
class.

.. code:: sh

   for i in range(10):
       task = StoppingTask(D=20, nGEN=100, optType=OptimizationType.MINIMIZATION, benchmark=MyBenchmark())

       # parameter is population size
       algo = GreyWolfOptimizer(NP=20)

       # running algorithm returns best found minimum
       best = algo.run(task)

       # printing best minimum
       print(best[-1])

Now we can run our advanced example with following command: *python
advanced_example.py*. The results should be similar to those bellow.

.. code:: sh

   7.606465129178389e-09
   5.288697102580944e-08
   6.875762169124336e-09
   1.386574251424837e-08
   2.174923591233085e-08
   2.578545710051624e-09
   1.1400628541972142e-08
   2.99387377733644e-08
   7.029492316948289e-09
   7.426212520156997e-09

For more usage examples please look at `examples </examples>`__ folder.

More advanced examples can also be found in the `NiaPy-examples
repository <https://github.com/NiaOrg/NiaPy-examples>`__.

Cite us
=======

Are you using NiaPy in your project or research? Please cite us!

Plain format
------------

::

         Vrbančič, G., Brezočnik, L., Mlakar, U., Fister, D., & Fister Jr., I. (2018).
         NiaPy: Python microframework for building nature-inspired algorithms.
         Journal of Open Source Software, 3(23), 613\. <https://doi.org/10.21105/joss.00613>

Bibtex format
-------------

::

       @article{NiaPyJOSS2018,
           author  = {Vrban{\v{c}}i{\v{c}}, Grega and Brezo{\v{c}}nik, Lucija
                     and Mlakar, Uro{\v{s}} and Fister, Du{\v{s}}an and {Fister Jr.}, Iztok},
           title   = {{NiaPy: Python microframework for building nature-inspired algorithms}},
           journal = {{Journal of Open Source Software}},
           year    = {2018},
           volume  = {3},
           issue   = {23},
           issn    = {2475-9066},
           doi     = {10.21105/joss.00613},
           url     = {https://doi.org/10.21105/joss.00613}
       }

RIS format
----------

::

       TY  - JOUR
       T1  - NiaPy: Python microframework for building nature-inspired algorithms
       AU  - Vrbančič, Grega
       AU  - Brezočnik, Lucija
       AU  - Mlakar, Uroš
       AU  - Fister, Dušan
       AU  - Fister Jr., Iztok
       PY  - 2018
       JF  - Journal of Open Source Software
       VL  - 3
       IS  - 23
       DO  - 10.21105/joss.00613
       UR  - http://joss.theoj.org/papers/10.21105/joss.00613


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!

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