Install the current PyPI release:
pip install opfunu
Or 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))
...
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If you see my code and data useful and use it, please cites my works here
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Nguyen, T., Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019). Efficient Time-Series Forecasting Using Neural Network and Opposition-Based Coral Reefs Optimization. International Journal of Computational Intelligence Systems, 12(2), 1144-1161.
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Nguyen, T., Tran, N., Nguyen, B. M., & Nguyen, G. (2018, November). A Resource Usage Prediction System Using Functional-Link and Genetic Algorithm Neural Network for Multivariate Cloud Metrics. In 2018 IEEE 11th Conference on Service-Oriented Computing and Applications (SOCA) (pp. 49-56). IEEE.
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Nguyen, T., Nguyen, B. M., & Nguyen, G. (2019, April). Building Resource Auto-scaler with Functional-Link Neural Network and Adaptive Bacterial Foraging Optimization. In International Conference on Theory and Applications of Models of Computation (pp. 501-517). Springer, Cham.
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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