def test_con_min(self):
# 1 worker
res = con_minimize(
fun=mishra_bird,
bounds=[(-10.0, 0.0), (-6.5, 0.0)],
constr=[mishra_bird_constr],
workers=1,
options={"generations": 3},
)
res = con_minimize(
fun=mishra_bird,
bounds=[(-10.0, 0.0), (-6.5, 0.0)],
constr=[mishra_bird_constr],
workers=2,
options={"generations": 3},
)
Global minimum at f(1., 1.) = 0.
"""
import logging
import numpy as np
import matplotlib.pyplot as plt
from modestga import con_minimize
from modestga.benchmark.functions.rosenbrock import rosenbrock_2par
from modestga.benchmark.functions.rosenbrock import rosenbrock_constr1
from modestga.benchmark.functions.rosenbrock import rosenbrock_constr2
# Set up logging
logging.basicConfig(
level='DEBUG',
filemode='w',
format="[%(processName)s][%(levelname)s] %(message)s"
)
# Run minimization
res = con_minimize(
fun=rosenbrock_2par,
bounds=[(-1.5, 1.5), (-0.5, 2.5)],
constr=[rosenbrock_constr1, rosenbrock_constr2],
workers=1,
options={
'tol': 1e-6
}
)
print(res)
"""Example of constrained optimization of the Mishra's Bird function.
Global minimum at f(-3.1302, -1.5821) = -106.7645
"""
import logging
import matplotlib.pyplot as plt
import numpy as np
from modestga import con_minimize
from modestga.benchmark.functions.mishra_bird import mishra_bird
from modestga.benchmark.functions.mishra_bird import mishra_bird_constr
# Set up logging
logging.basicConfig(level="DEBUG",
filemode="w",
format="[%(processName)s][%(levelname)s] %(message)s")
# Run minimization
res = con_minimize(
fun=mishra_bird,
bounds=[(-10.0, 0.0), (-6.5, 0.0)],
constr=[mishra_bird_constr],
workers=1,
)
print(res)
filemode="w",
format="[%(processName)s][%(levelname)s] %(message)s")
# Set up cost function
from modestga.benchmark.functions import rastrigin
fun = rastrigin
# Set up bounds (rectangular)
bounds = [(-5.12, 5.12) for i in range(8)]
# Constraints (algorithm will try to keep their outputs positive)
def fcon1(x, *args):
return x[1] - 1.0
def fcon2(x, *args):
return x[0] - 2.0
constr = (fcon1, fcon2)
# Genetic Algorithm options
options = {"generations": 1000, "pop_size": 500, "tol": 1e-3}
# Run minimization
res = con_minimize(fun, bounds, constr=constr, options=options)
print(res)