OPFUNU (OPtimization benchmark FUnctions in NUmpy) 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.
Please include these citations if you plan to use this library:
@article{Van_Thieu_2024_Opfunu,
author = {Van Thieu, Nguyen},
title = {Opfunu: An Open-source Python Library for Optimization Benchmark Functions},
doi = {10.5334/jors.508},
journal = {Journal of Open Research Software},
month = {May},
year = {2024}
}
APA:
Van Thieu, N. (2024). Opfunu: An Open-source Python Library for Optimization Benchmark Functions. Journal of Open Research Software, 12(1), 8. https://doi.org/10.5334/jors.508
Install the current PyPI release:
$ pip install opfunu
After installation, you can import and check version of Opfunu:
$ python
>>> import opfunu
>>> opfunu.__version__
>>> dir(opfunu)
>>> help(opfunu)
>>> opfunu.FUNC_DATABASE # List all name_based functions
>>> opfunu.CEC_DATABASE # List all cec_based functions
>>> opfunu.ALL_DATABASE # List all functions in this library
>>> opfunu.get_functions_by_classname("MiShra04")
>>> opfunu.get_functions_based_classname("2015")
>>> opfunu.get_functions_by_ndim(2)
>>> opfunu.get_functions_based_ndim(50)
>>> opfunu.get_name_based_functions(ndim=10, continuous=True)
>>> opfunu.get_cec_based_functions(ndim=2)
Let's go through some examples.
How to get the function and use it
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())
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)
Two ways if you want to draw functions that available in Opfunu.
from opfunu.cec_based import F12010
f0 = F12010()
# Visualize opfunu function using method in object
f0.plot_2d(selected_dims=(2, 3), n_points=300, ct_cmap="viridis", ct_levels=30, ct_alpha=0.7,
fixed_strategy="mean", fixed_values=None, title="Contour map of the F1 CEC 2010 function",
x_label=None, y_label=None, figsize=(10, 8), filename="2d-f12010", exts=(".png", ".pdf"), verbose=True)
f0.plot_3d(selected_dims=(1, 6), n_points=500, ct_cmap="viridis", ct_levels=30, ct_alpha=0.7,
fixed_strategy="mean", fixed_values=None, title="3D visualization of the F1 CEC 2010 function",
x_label=None, y_label=None, figsize=(10, 8), filename="3d-f12010", exts=(".png", ".pdf"), verbose=True)
## Visualize opfunu function using utility function
from opfunu import draw_2d, draw_3d
draw_2d(f0.evaluate, f0.lb, f0.ub, selected_dims=(2, 3), n_points=300)
draw_3d(f0.evaluate, f0.lb, f0.ub, selected_dims=(2, 3), n_points=300)
F1-2010 CEC 2D |
F1-2010 CEC 3D |
Two ways if you want to draw latex equation.
from opfunu.cec_based import F12010
from opfunu.name_based import Ackley02
from opfunu.utils.visualize import draw_latex
f0 = F12010()
f1 = Ackley02()
## Plot using function inside the object
f0.plot_latex(f0.latex_formula, figsize=(8, 3), dpi=500, title="Latex equation", exts=(".png", ".pdf"), verbose=True)
f1.plot_latex(f1.latex_formula_global_optimum, figsize=(8, 3), dpi=500, title="Global optimum", verbose=True)
## Plot using module
draw_latex(f0.latex_formula_bounds, title="Boundary for Function")
draw_latex(f1.latex_formula_dimension, title=None)
For more usage examples please look at examples folder.
There are lots of ways how you can contribute to Permetrics's development, and you are welcome to join in! For example, you can report problems or make feature requests on the issues pages. To facilitate contributions, please check for the guidelines in the CONTRIBUTING.md file.
Developed by: Thieu @ 2023