NiaPy

Nature-inspired algorithms are a very popular tool for solving optimization problems. Numerous variants of nature-inspired algorithms have been developed (paper 1, paper 2) 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 offers:

  • numerous optimization problem 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.

Installation

Install NiaPy with pip:

Latest version (2.0.0rc16)

$ pip install niapy==2.0.0rc16

To install NiaPy with conda, use:

$ conda install -c niaorg niapy=2.0.0rc16

Latest stable version

$ pip install niapy

To install NiaPy with conda, use:

$ conda install -c niaorg niapy

To install NiaPy on Fedora, use:

$ dnf install python3-niapy

Install from source

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

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

Algorithms

Click here for the list of implemented algorithms.

Usage

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

$ 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 the Pintér problem 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 the complete source code of basic example.

from niapy.algorithms.basic import GreyWolfOptimizer
from niapy.task import Task

# we will run 10 repetitions of Grey Wolf Optimizer against the Pinter problem
for i in range(10):
    task = Task(problem='pinter', dimension=10, max_evals=1000)
    algorithm = GreyWolfOptimizer(population_size=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:

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 a custom problem class 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 optimization function, we have to create new class. Let's name it MyProblem. In the initialization
method of MyProblem class we have to set the dimension, lower and upper bounds of the problem. Afterwards we have to
override the abstract method _evaluate which takes a parameter x, the solution to be evaluated, and returns the function value.
Now we should have something similar as is shown in code snippet bellow.

import numpy as np
from niapy.task import Task
from niapy.problems import Problem
from niapy.algorithms.basic import GreyWolfOptimizer


# our custom problem class
class MyProblem(Problem):
    def __init__(self, dimension, lower=-10, upper=10, *args, **kwargs):
        super().__init__(dimension, lower, upper, *args, **kwargs)

    def _evaluate(self, x):
        return np.sum(x ** 2)

Now, all we have to do is to initialize our algorithm as in previous examples and pass an instance of our MyProblem class as the problem argument.

my_problem = MyProblem(dimension=20)
for i in range(10):
    task = Task(problem=my_problem, max_iters=100)
    algo = GreyWolfOptimizer(population_size=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.

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 folder.

More advanced examples can also be found in the NiaPy-examples repository.

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

GitHub

https://github.com/NiaOrg/NiaPy