ENOPPY (ENgineering Optimization Problems in PYthon) is the largest python library for real-world engineering optimization problems. Contains all engineering problems from CEC competition functions from 2005, 2008, 2010, 2013, 2014, 2015, 2017, 2019, 2020, 2021, 2022.

• Free software: GNU General Public License (GPL) V3 license
• Total problems: > 50 problems
• Documentation: https://enoppy.readthedocs.io/en/latest/
• Python versions: 3.6.x, 3.7.x, 3.8.x, 3.9.x, 3.10.x
• Dependencies: numpy, scipy, pandas, matplotlib

Installation

Install with pip

Install the current PyPI release:

$ pip install enoppy==0.1.0

Or install the development version from GitHub:

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

Install from source

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

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

Lib's structure

docs
examples
enoppy
    paper_based
        pdo_2022.py
        rwco_2020.py
    problem_based
        chemical.py
        mechanism.py
    utils
        operator.py
        validator.py
        visualize.py
    __init__.py
    engineer.py
README.md
setup.py

Usage

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

$ python
>>> import enoppy
>>> enoppy.__version__

Let's go through some examples.

Examples

How to get the function and use it

1st way

from enoppy.cec_based.cec2014 import F12014

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

## or

from enoppy.cec_based import F102014

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

2nd way

import enoppy

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

## or

all_funcs_2014 = enoppy.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/enoppy

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

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

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

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

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

• 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/permetrics
  • https://github.com/aiir-team

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Cite Us

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

@software{thieu_nguyen_2020_3711682,
  author       = {Nguyen Van Thieu},
  title        = {ENOPPY: A Python Library for Engineering Optimization Problems},
  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