A python implementation of optimization benchmarks
Project description
# PyBenchFCN #
A python implementation of optimization benchmarks
How to Install
This library is a python implementation for the MatLab package BenchmarkFcns Toolbox.
You can simply install with command pip install PyBenchFCN
.
- Pre-request:
numpy
,matplotlib
How to Use
The input of each numerical optimization problem could be a 1-D ndarray, or 2-D ndarray.
- 1-D array
- an example of a solution (individual) for 10D problem is
np.random.uniform(0, 1, 10)
, where each entry is a decision variable. - use
f()
to return a fitness value (scalar for SOP, 1D-array for MOP).
- an example of a solution (individual) for 10D problem is
- 2-D array
- an example of group of solutions (population) for 10D problem is
np.random.uniform(0, 1, (5, 10))
, where each row (totally 5) is an individual. - use
F()
to return an array of fitness value (1-D array for SOP, 2-D array for MOP).
- an example of group of solutions (population) for 10D problem is
Set Benchmark Function
To set a benchmark function, one may see the sample code in Factory.py
in the repository, or follow the script below. Also, there is a sample optimization program provided in sample.py
.
import numpy as np
# import single objective problems from PyBenchFCN
from PyBenchFCN import SingleObjectiveProblem as SOP
n_var = 10 # dimension of problem
n_pop = 3 # size of population
problem = SOP.ackleyfcn(n_var) # Ackley problem
'''same function as the code above
from PyBenchFCN import Factory
problem = Factory.set_sop("f1", n_var)
'''
print( np.round(problem.optimalF, 5) ) # show rounded optimal value
xl, xu = problem.boundaries # bound of problem
x = np.random.uniform(xl, xu, n_var) # initialize a solution
print( problem.f(x) ) # show fitness value as scalar
X = np.random.uniform( xl, xu, (n_pop, n_var) ) # initialize a population
print( problem.F(X) ) # show fitness values as 1d-array
Plot Fitness Landscape
To plot a fitness landscape (2D space), one can use the code below. Notice, this function only works for continuous SOPs.
from PyBenchFCN import Tool
Tool.plot_sop("sphere", mode="save") # plot and save landscape of Sphere function
Tool.plot_sop("schwefel", plot_type="contour") # plot contour plot of Schwefel function
List of Functions
Classical Single-Objective Optimization
Totally, 61 single-objective functions are implemented. The plot of 2D versions of 59 problems are provided. Please check the homepage of BenchmarkFcns Toolbox or this website for the further documentation.
Discrete Optimization
Under development ...
Multi-Objective Optimization
Under development ...
Real-World Optimization
Under development ...
Authors
Yifan He @ Dept. of CS, UTsukuba
License
This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgement
PyBenchFCN is maintained by Yifan He. The author of this repostory is very grateful to Mr. Mazhar Ansari Ardeh, who implemented the MatLab package BenchFCNs Toolbox.
- If you find any mistakes, please report at a new issue.
- If you want to help me implement more benchmarks (discrete optimization, multi-objective optimization), please contact at he.yifan.xs@alumni.tsukuba.ac.jp.
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