def main():
suite = 'rw-top-trumps'
function = 5
instance = 5
dim = 128
nobj = 1
rep = 2000
name = suite + '_f' + str(function) + 'i' + str(instance) + 'd' + str(dim)
problem = tt_problem(suite, name, dim, nobj, function, instance, rep)
minimize_plot(problem,
random_search(10000),
name + '_10k64',
num_retries=64)
minimize_plot(problem, Cma_cpp(10000), name + '_10k64', num_retries=64)
minimize_plot(problem, De_cpp(10000), name + '_10k64', num_retries=64)
minimize_plot(problem,
Bite_cpp(10000, M=16),
name + '_10k64',
num_retries=64)
suite = 'rw-top-trumps-biobj'
function = 3
instance = 5
dim = 128
nobj = 2
rep = 2000
name = suite + '_f' + str(function) + 'i' + str(instance) + 'd' + str(dim)
problem = tt_problem(suite, name, dim, nobj, function, instance, rep)
mo_minimize_plot(problem,
random_search(4000),
name + '_4k512',
num_retries=512)
mo_minimize_plot(problem, Cma_cpp(4000), name + '_4k512', num_retries=512)
mo_minimize_plot(problem, De_cpp(4000), name + '_4k512', num_retries=512)
mo_minimize_plot(problem,
Bite_cpp(4000, M=16),
name + '_4k512',
num_retries=512)
mode.minimize_plot(name,
problem.fun,
problem.bounds,
2,
popsize=200,
nsga_update=True,
max_eval=1000000)
mode.minimize_plot(name,
problem.fun,
problem.bounds,
2,
popsize=200,
nsga_update=False,
max_eval=1000000)
def test_cma_cordinated_retry(dim=6):
# coordinated retry with CMA-ES optimizer with reduced popsize
# faster for small dimension, use default for dim > 12
return advretry.minimize(problem.fun,
problem.bounds,
logger=logger(),
optimizer=Cma_cpp(2000, popsize=13))
def test_gclde_cordinated_retry(problem):
# coordinated retry with GCLDE->CMA sequence optimizer
return advretry.minimize(problem.fun,
problem.bounds,
logger=logger(),
optimizer=Sequence(
[GCLDE_cpp(750),
Cma_cpp(750, popsize=13)]))
def test_gclde_cordinated_retry(dim=6):
# coordinated retry with GCLDE->CMA sequence optimizer
return advretry.minimize(obj_f_c,
bounds(dim),
logger=logger(),
optimizer=Sequence(
[GCLDE_cpp(750),
Cma_cpp(750, popsize=13)]))
def de_pyg_cma(max_evaluations=50000,
popsize=31,
stop_fitness=-math.inf,
de_max_evals=None,
cma_max_evals=None):
"""Sequence de c++ -> cmaes pagmo."""
deEvals = np.random.uniform(0.1, 0.3)
if de_max_evals is None:
de_max_evals = int(deEvals * max_evaluations)
if cma_max_evals is None:
cma_max_evals = int((1.0 - deEvals) * max_evaluations)
opt1 = De_pyg(popsize=popsize,
max_evaluations=de_max_evals,
stop_fitness=stop_fitness)
opt2 = Cma_cpp(popsize=popsize,
max_evaluations=cma_max_evals,
stop_fitness=stop_fitness)
return Sequence([opt1, opt2])
problems = [
Cassini1(),
Cassini2(),
Rosetta(),
Tandem(5),
Messenger(),
Gtoc1(),
MessFull(),
Sagas(),
Cassini1minlp()
]
algos = [
de2_cma(min_evals),
de_cma(min_evals),
da_cma(min_evals),
Cma_cpp(min_evals),
De_cpp(min_evals),
Hh_cpp(min_evals),
Da_cpp(min_evals),
Dual_annealing(min_evals),
Differential_evolution(min_evals)
]
def messengerFullLoop(opt, num, max_time=1200, log=logger()):
problem = MessFull()
minimizers = None # remote actors created by minimize will be reused
log.info(problem.name + ' ' + opt.name)
for i in range(num):
ret, minimizers = minimize(problem.fun,
problem.bounds,
# Copyright (c) Dietmar Wolz.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory.
import math
from fcmaes.astro import MessFull, Messenger, Gtoc1, Cassini1, Cassini2, Rosetta, Tandem, Sagas
from fcmaes.optimizer import logger, de_cma, da_cma, Cma_cpp, De_cpp, Da_cpp, Dual_annealing, Differential_evolution
from fcmaes.retry import minimize
problems = [Cassini1(), Cassini2(), Rosetta(), Tandem(5), Messenger(), Gtoc1(), MessFull(), Sagas()]
max_evals = 50000
algos = [de_cma(max_evals), da_cma(max_evals), Cma_cpp(max_evals), De_cpp(max_evals),
Da_cpp(max_evals), Dual_annealing(max_evals), Differential_evolution(max_evals)]
def test_all(num_retries = 10000, num = 10):
for problem in problems:
for algo in algos:
_test_optimizer(algo, problem, num_retries, num)
def _test_optimizer(opt, problem, num_retries = 32, num = 10):
log = logger()
log.info(problem.name + ' ' + opt.name)
for _ in range(num):
ret = minimize(problem.fun, problem.bounds, math.inf, num_retries, log, optimizer=opt)
def main():
test_all()
Rosetta(),
Tandem(5),
Messenger(),
Gtoc1(),
MessFull(),
Sagas(),
Cassini1minlp()
]
max_evals = 50000
algos = [
de_cma(max_evals),
de2_cma(max_evals),
da_cma(max_evals),
Cma_cpp(max_evals),
De_cpp(max_evals),
Hh_cpp(max_evals),
Da_cpp(max_evals),
Bite_cpp(max_evals),
Csma_cpp(max_evals),
Dual_annealing(max_evals),
Differential_evolution(max_evals)
]
def test_all(num_retries=10000, num=1):
for problem in problems:
for algo in algos:
_test_optimizer(algo, problem, num_retries, num)