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OPFUNU is the largest python library for cutting-edge numerical optimization benchmark functions. Contains all CEC competition functions from 2005, 2008, 2010, 2013, 2014, 2015, 2017, 2019, 2020, 2021, 2022. Besides, more than 300 traditional functions with different dimensions are implemented.

• Free software: GNU General Public License (GPL) V3 license
• Total problems: > 500 problems
• Documentation: https://opfunu.readthedocs.io
• Python versions: 3.7.x, 3.8.x, 3.9.x, 3.10.x, 3.11.x
• Dependencies: numpy, matplotlib

Installation

Install with pip

Install the current PyPI release:

$ pip install opfunu==1.0.1

Or install the development version from GitHub:

pip install git+https://github.com/thieu1995/opfunu

Install from source

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

$ git clone https://github.com/thieu1995/opfunu.git
$ cd opfunu
$ python setup.py install

Lib's structure

docs
examples
opfunu
    cec_based
        cec.py
        cec2005.py
        cec2008.py
        ...
        cec2021.py
        cec2022.py
    name_based
        a_func.py
        b_func.py
        ...
        y_func.py
        z_func.py
    utils
        operator.py
        validator.py
        visualize.py
    __init__.py
    benchmark.py
README.md
setup.py

Usage

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

$ python
>>> import opfunu
>>> opfunu.__version__

Let's go through some examples.

Examples

How to get the function and use it

1st way

from opfunu.cec_based.cec2014 import F12014

func = F12014(ndim=30)
func.evaluate(func.create_solution())

## or

from opfunu.cec_based import F102014

func = F102014(ndim=50)
func.evaluate(func.create_solution())

2nd way

import opfunu

funcs = opfunu.get_functions_by_classname("F12014")
func = funcs[0](ndim=10)
func.evaluate(func.create_solution())

## or

all_funcs_2014 = opfunu.get_functions_based_classname("2014")
print(all_funcs_2014)

For more usage examples please look at examples folder.

Get helps (questions, problems)

• Official source code repo: https://github.com/thieu1995/opfunu

• Official document: https://opfunu.readthedocs.io/

• Download releases: https://pypi.org/project/opfunu/

• Issue tracker: https://github.com/thieu1995/opfunu/issues

• Notable changes log: https://github.com/thieu1995/opfunu/blob/master/ChangeLog.md

• Examples with different meapy version: https://github.com/thieu1995/opfunu/blob/master/examples.md

• Join our telegram community at link** to get more support and help.

• This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here

  • https://github.com/thieu1995/mealpy
  • https://github.com/thieu1995/metaheuristics
  • https://github.com/thieu1995/enoppy
  • https://github.com/thieu1995/permetrics
  • https://github.com/aiir-team

Cite Us

If you are using opfunu in your project, we would appreciate citations:

@software{thieu_nguyen_2020_3711682,
  author       = {Nguyen Van Thieu},
  title        = {Opfunu: An Open-source Library For Optimization Functions in Numpy},
  year         = 2020,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.3620960},
  url          = {https://doi.org/10.5281/zenodo.3620960.}
}

References

1. http://benchmarkfcns.xyz/fcns
2. https://en.wikipedia.org/wiki/Test_functions_for_optimization
3. https://www.cs.unm.edu/~neal.holts/dga/benchmarkFunction/
4. http://www.sfu.ca/~ssurjano/optimization.html
5. A Literature Survey of Benchmark Functions For Global Optimization Problems (2013)
6. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization