Install the current PyPI release:
pip install opfunuOr install the development version from GitHub:
pip install git+https://github.com/thieunguyen5991/opfunu- All you need to do is: (Make sure your solution is a numpy 1-D array)
## For dimension_based
from opfunu.dimension_based.benchmark2d import Functions # import 2-d benchmark functions
import numpy as np
solution2d = np.array([-0.1, 1.5]) # Solution for 2-d benchmark
func2d = Functions() # create an object
print(func2d._bartels_conn__(solution2d)) # using function in above object
print(func2d._bird__(solution2d))
## For type_based (same as dimension_based)
from opfunu.type_based.multi_modal import Functions # import 2-d benchmark functions
import numpy as np
## For CEC
from opfunu.cec.cec2014 import Functions # import cec2014 functions
import numpy as np
cec_sol = np.array([-0.1, 1.5]) # Solution for 2-d benchmark
cec_func = Functions() # create an object
print(cec_func.C1(cec_sol)) # using function in above object from C1, ..., C30
print(cec_func.C30(cec_sol))
## CEC-2005 or CEC-2008
import numpy as np
from opfunu.cec.cec2005.F1 import Model as f1
from opfunu.cec.cec2008.F7 import Model as f7
solution = np.array([0.5, 1, 1.5, 2, 3, 0.9, 1.2, 2, 1, 5])
t1 = f1()
result = t1._main__(temp)
print(result)
t2 = f7()
result = t2._main__(temp)
print(result)
## CEC-2010
import numpy as np
from opfunu.cec.cec2010.function import F1, F2, ..., F12,..
solution = np.random.uniform(0, 1, 1000)
result = F12(temp)
print(result)
## CEC-2013 (2 ways to use depend on your purpose)
import numpy as np
from opfunu.cec.cec2013.unconstraint import Model as M13
from opfunu.cec.cec2014.unconstraint2 import Model as MD2
problem_size = 10
solution = np.random.uniform(0, 1, problem_size)
obj = MD2(problem_size) # Object style solve different problems with different functions
print(obj.F1(solution))
print(obj.F2(solution))
obj = M13(solution) # Object style solve same problem with every functions
print(obj.F1())
print(obj.F2())
## CEC-2014 (3 ways to use depend on your purpose)
import numpy as np
from opfunu.cec.cec2014.function import F1, F2, ...
from opfunu.cec.cec2014.unconstraint2 import Model as MD2
from opfunu.cec.cec2014.unconstraint import Model as MD
problem_size = 10
solution = np.random.uniform(0, 1, problem_size)
print(F1(solution)) # Function style
func = MD(problem_size) # Object style solve different problems with different functions
print(func.F1(solution))
print(func.F2(solution))
obj = MD2(solution) # Object style solve same problem with every functions
print(obj.F1())
print(obj.F2())
## CEC-2015
import numpy as np
from opfunu.cec.cec2015.function import F1, F2,...
temp = np.random.uniform(0, 1, 10)
result = F1(temp)
print(result)
## CEC basic
import numpy as np
from opfunu.cec_basic.cec2014 import *
problem_size = 20
sol = np.random.uniform(0, 1, 20)
print(F30(sol))
...- If you see my code and data useful and use it, please cites my works here
@software{thieu_nguyen_2020_3711682,
author = {Thieu Nguyen},
title = {A framework of un-constrained Optimization Functions in Numpy (OpFuNu) for global optimization
problems},
month = march,
year = 2020,
publisher = {Zenodo},
doi = {10.5281/zenodo.3620960},
url = {https://doi.org/10.5281/zenodo.3620960.}
}
@article{nguyen2019efficient,
title={Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization},
author={Nguyen, Thieu and Nguyen, Tu and Nguyen, Binh Minh and Nguyen, Giang},
journal={International Journal of Computational Intelligence Systems},
volume={12},
number={2},
pages={1144--1161},
year={2019},
publisher={Atlantis Press}
}
- This project related to my another projects which are "meta-heuristics" and "neural-network", check it here
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