def adv_minimize_plot(name, optimizer, fun, bounds,
value_limit = math.inf, num_retries = 1024, logger=logger(), statistic_num = 0):
time0 = time.perf_counter() # optimization start time
name += '_smart_' + optimizer.name
logger.info('smart optimize ' + name)
store = advretry.Store(lambda x:fun(x)[0], bounds, capacity=5000, logger=logger,
num_retries=num_retries, statistic_num = statistic_num)
advretry.retry(store, optimizer.minimize, value_limit)
xs = np.array(store.get_xs())
ys = np.array([fun(x) for x in xs])
retry.plot(ys, '_all_' + name + '.png', interp=False)
np.savez_compressed(name , xs=xs, ys=ys)
xs, front = pareto(xs, ys)
logger.info(name+ ' time ' + str(dtime(time0)))
retry.plot(front, '_front_' + name + '.png')
def _test_optimizer(opt_name, problem, num_retries = 4000, num = 20, value_limit = 20.0,
log = logger()):
log.info(problem.name + ' ' + opt_name)
for i in range(num):
store = Store(problem.bounds, logger = log)
optimizer = Optimizer(store, 0)
method = getattr(optimizer, opt_name)
ret = retry(problem.fun, store, method, num_retries, value_limit = value_limit)
def optimize():
name = 'tsin3000.60' # 60 trajectories to choose from
# name = 'tsin3000.10' # 10 fixed trajectories
transfers = pd.read_csv('data/' + name + '.xz',
sep=' ',
usecols=[1, 2, 3, 4, 5, 6, 7],
compression='xz',
names=[
'asteroid', 'station', 'trajectory', 'mass',
'dv', 'transfer_start', 'transfer_time'
])
# uncomment to write a clear text csv
# transfers.to_csv('data/' + name + '.txt', sep=' ', header=False)
global TRAJECTORY_NUM, ASTEROID_NUM # adjust number of asteroids / trajectories
TRAJECTORY_NUM = int(np.amax(transfers["trajectory"]) + 1)
ASTEROID_NUM = int(np.amax(transfers["asteroid"]) + 1)
# bounds for the objective function
dim = 10 + 2 * STATION_NUM - 1
lower_bound = np.zeros(dim)
# lower_bound[10+STATION_NUM:dim] = 0.00001
upper_bound = np.zeros(dim)
lower_bound[:] = 0.0000001
upper_bound[10:] = 0.9999999
upper_bound[:10] = TRAJECTORY_NUM - 0.00001 # trajectory indices
bounds = Bounds(lower_bound, upper_bound)
# smart boundary management (SMB) with DE->CMA
store = advretry.Store(fitness(transfers),
bounds,
num_retries=10000,
max_eval_fac=5.0,
logger=logger())
advretry.retry(store, de_cma(10000).minimize)
# smart boundary management (SMB) with CMA-ES
# store = advretry.Store(fitness(transfers), bounds, num_retries=10000, max_eval_fac=5.0, logger=logger())
# advretry.retry(store, Cma_cpp(10000).minimize)
# BiteOpt algorithm multi threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, Bite_cpp(1000000, M=1).minimize, num_retries=3200)
# CMA-ES multi threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, Cma_cpp(1000000).minimize, num_retries=3200)
# scipy minimize algorithm multi threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, Minimize(1000000).minimize, num_retries=3200)
# fcmaes differential evolution multi threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, De_cpp(1000000).minimize, num_retries=3200)
# scipy differential evolution multi threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, Differential_evolution(1000000).minimize, num_retries=3200)
# scipy dual annealing multi threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, Dual_annealing(1000000).minimize, num_retries=3200)
# scipy differential evolution single threaded
# store = retry.Store(fitness(transfers), bounds, logger=logger())
# retry.retry(store, Differential_evolution(1000000).minimize, num_retries=320, workers=1)
return store.get_xs(), store.get_ys()
def retry(self, optimizer):
retries = self.retries + self.num_retries
self.ret = advretry.retry(self.fun, self.store, optimizer.minimize,
retries)
self.retries = retries
self.value = self.store.get_y_best()