# 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()
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,
max_nodes,
None,
num_retries=20000,
value_limit=12.0,
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)
def _test_optimizer(opt, problem, num_retries=32, num=1):
log = logger()
log.info(problem.name + ' ' + opt.name)
for _ in range(num):
minimize(problem.fun,