Optimization Test Problems
Project description
Installation
The test problems are uploaded to the PyPi Repository.
pip install pymop
Usage
# numpy arrays are required as an input
import numpy as np
# first import the specific problem to be solved
from pymop.dtlz import DTLZ1
# initialize it with the necessary parameters
problem = DTLZ1(n_var=10, n_obj=3)
# evaluation function returns by default two numpy arrays - objective function values and constraints -
# as input either provide a vector
F, G = problem.evaluate(np.random.random(10))
# or a whole matrix to evaluate several solutions at once
F, G = problem.evaluate(np.random.random((100, 10)))
# if no constraints should be returned
F = problem.evaluate(np.random.random((100, 10)), return_constraints=0)
# if only the constraint violation should be returned - vector of zeros if no constraints exist
from pymop.welded_beam import WeldedBeam
problem = WeldedBeam()
F, CV = problem.evaluate(np.random.random((100, 4)), return_constraints=2)
Problems
In this package single- as well as multi-objective test problems are included.
Single-Objective:
Ackley
BNH
Griewank
Knapsack
Schwefel
Sphere
Zakharov
Multi-Objective:
DTLZ 1-7
ZDT 1-6
Carside Impact
BNH
Kursawe
OSY
TNK
Welded Beam
Implementation
All problems are implemented to efficiently evaluate multiple input points at a time. Therefore, the input can be a n x m dimensional matrix, where n is the number of points to evaluate and m the number of variables.
Contact
Feel free to contact me if you have any question: blankjul@egr.msu.edu
Project details
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