- The structure of version 1.0.0 is based on Object-Oriented Programming, which is entirely different from the previous version (<= 0.8.0).
- All CEC functions from 2005, 2008, 2010, 2013, 2014, 2015, 2017, 2019, 2020, 2021, 2022 are implemented. This version is well-organized, faster and has no more bugs.
- All old code-based functions from previous version <= 0.8.0 will be removed in version 1.0.1
Install the current PyPI release:
pip install opfunu==1.0.0
Or install the development version from GitHub:
pip install git+https://github.com/thieu1995/opfunu
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
- 1st way
from opfunu.cec_based.cec2014 import F12014
func = F12014(ndim=30)
func.evaluate(func.create_solution())
## or
from opfunu.cec_based import F12014
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)
- If you see my code and data useful and use it, please cite my works here
@software{thieu_nguyen_2020_3711682,
author = {Thieu Nguyen},
title = {A framework of Optimization Functions using Numpy (OpFuNu) for optimization problems},
year = 2020,
publisher = {Zenodo},
doi = {10.5281/zenodo.3620960},
url = {https://doi.org/10.5281/zenodo.3620960.}
}
1. dimension_based 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
2. type_based
A Literature Survey of Benchmark Functions For Global Optimization Problems (2013)
3. cec
Problem Definitions and Evaluation Criteria for the CEC 2014
Special Session and Competition on Single Objective Real-Parameter Numerical Optimization