def run_example3():
import pygmo as pg
from pykep.examples import add_gradient, algo_factory
# problem
udp = add_gradient(mga_lt_EVMe(), with_grad=False)
prob = pg.problem(udp)
prob.c_tol = [1e-5] * prob.get_nc()
# algorithm
uda = algo_factory("snopt7", False)
uda2 = pg.mbh(uda, 5, 0.05)
algo = pg.algorithm(uda2)
algo.set_verbosity(1)
# 3 - Population
pop = pg.population(prob, 1)
# 4 - Solve the problem (evolve)
print("Running Monotonic Basin Hopping ....")
pop = algo.evolve(pop)
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(pop.champion_x)))
udp.udp_inner.plot(pop.champion_x)
def run_example4():
import pygmo as pg
from pykep.examples import add_gradient, algo_factory
N = 20
# problem
udp = add_gradient(mga_lt_earth_mars_sundmann(nseg=N), with_grad=False)
prob = pg.problem(udp)
prob.c_tol = [1e-5] * prob.get_nc()
# algorithm
uda = algo_factory("snopt7", False)
uda2 = pg.mbh(uda, 5, 0.05)
algo = pg.algorithm(uda2)
algo.set_verbosity(1)
# 3 - Population
pop = pg.population(prob, 1)
# 4 - Solve the problem (evolve)
print("Running Monotonic Basin Hopping ....")
pop = algo.evolve(pop)
print("Is the solution found a feasible trajectory? " +
str(prob.feasibility_x(pop.champion_x)))
udp.udp_inner.plot(pop.champion_x)
def method_b():
algo = pg.algorithm(uda=pg.mbh(pg.nlopt("slsqp"), stop=20, perturb=.2))
print(algo)
algo.set_verbosity(
1) # in this case this correspond to logs each 1 call to slsqp
pop = pg.population(prob=UdpConstrained(), size=1)
pop.problem.c_tol = [1E-6] * 6
pop = algo.evolve(pop)
def run_rui2016(**kwargs):
# a new optimisation problem
n_rb = kwargs['n_rb']
n_cc = kwargs['n_cc']
n_pairs = kwargs['n_pairs']
prob = Rui2016(**kwargs)
# start simulation
# prob.gen_cc_ues()
# prob.gen_d2d_pairs()
# start optimisation
nl = pg.nlopt('slsqp')
nl.xtol_rel = 1e-3
algo = pg.algorithm(uda=pg.mbh(nl, stop=3))
pop = pg.population(prob=prob, size=1)
pop.problem.c_tol = [1e-3] * (n_cc + n_pairs)
pop = algo.evolve(pop)
return pop
def optimize(self,
niter=500,
minimizer_kwargs=None,
nmin=1000,
kforce=100.,
gradient=False,
print_fun=None,
popsize=50,
stepsize=0.05,
optimizer="evolution",
seed=None):
self.kforce = kforce
if type(seed) == type(None):
seed = np.random.randint(999999)
else:
seed = int(seed)
np.random.seed(seed)
pygmo.set_global_rng_seed(seed=seed)
self.set_x0()
bounds = gist_bounds(self.xmin, self.xmax, Tconst=True)
min_bounds = bounds.get_bounds_for_minimizer()
if optimizer == "evolution":
### This works , because pygmo makes deepcopies of this object
### in order to remain "thread safe" during all following operations
prob = pygmo.problem(self)
if self.decomp:
if (popsize % 4) != 0:
popsize = (popsize / 4) * 4
if popsize < 5:
popsize = 8
pop = pygmo.population(prob=prob, size=popsize)
if self.decomp:
### For NSGA2, popsize must be >4 and also
### a multiple of four.
algo = pygmo.algorithm(pygmo.nsga2(gen=niter))
#algo = pygmo.algorithm(pygmo.moead(gen=niter))
else:
algo = pygmo.algorithm(pygmo.sade(gen=niter))
if self.verbose:
algo.set_verbosity(1)
pop = algo.evolve(pop)
for x in pop.get_x():
print_fun(x)
print_fun.flush()
elif optimizer == "brute":
self.anal_grad = False
self.anal_boundary = False
N_dim = self._x0.size
niter_count = np.zeros(self._x0.size, dtype=int)
for i in range(self._x0.size):
self._x0[i] = min_bounds[i][0]
_diff = min_bounds[i][1] - min_bounds[i][0]
niter_count[i] = int(_diff / stepsize)
prop = propagator(self._x0, N_dim, stepsize, niter_count)
stop = False
_stop = False
if nmin > 0:
self.anal_grad = True
self.anal_boundary = False
prob = pygmo.problem(self)
pop = pygmo.population(prob=prob, size=1)
algo = pygmo.algorithm(pygmo.nlopt("slsqp"))
algo.maxeval = nmin
if self.verbose:
algo.set_verbosity(1)
while (not stop):
if nmin > 0:
self.anal_grad = gradient
if self.anal_boundary:
min_bounds = None
bounds = None
pop.set_x(0, self._x0)
pop = algo.evolve(pop)
x = pop.get_x()[0]
else:
x = self._x0
if print_fun != None:
print_fun(x)
print_fun.flush()
### propagate self._x0
prop.add()
if _stop:
stop = True
_stop = prop.are_we_done()
elif optimizer == "basinhopping":
prob = pygmo.problem(self)
pop = pygmo.population(prob=prob, size=popsize)
algo = pygmo.algorithm(uda=pygmo.mbh(
pygmo.nlopt("slsqp"), stop=100, perturb=self.steps * 0.1))
if self.verbose:
algo.set_verbosity(1)
pop = algo.evolve(pop)
for x in pop.get_x():
print_fun(x)
print_fun.flush()
else:
raise ValueError("Optimizer %s is not known." % optimizer)
def __call__(self, function):
scanner_options = {
'sade':
dict(gen=self.gen,
variant=self.variant,
variant_adptv=self.variant_adptv,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
seed=self.seed),
'gaco':
dict(gen=self.gen,
ker=self.ker,
q=self.q,
oracle=self.oracle,
acc=self.acc,
threshold=self.threshold,
n_gen_mark=self.n_gen_mark,
impstop=self.impstop,
evalstop=self.evalstop,
focus=self.focus,
memory=self.memory,
seed=self.seed),
'maco':
dict(gen=self.gen,
ker=self.ker,
q=self.q,
threshold=self.threshold,
n_gen_mark=self.n_gen_mark,
evalstop=self.evalstop,
focus=self.focus,
memory=self.memory,
seed=self.seed),
'gwo':
dict(gen=self.gen, seed=self.seed),
'bee_colony':
dict(gen=self.gen, limit=self.limit, seed=self.seed),
'de':
dict(gen=self.gen,
F=self.F,
CR=self.CR,
variant=self.variant,
ftol=self.ftol,
xtol=self.xtol,
seed=self.seed),
'sea':
dict(gen=self.gen, seed=self.seed),
'sga':
dict(gen=self.gen,
cr=self.cr,
eta_c=self.eta_c,
m=self.m,
param_m=self.param_m,
param_s=self.param_s,
crossover=self.crossover,
mutation=self.mutation,
selection=self.selection,
seed=self.seed),
'de1220':
dict(gen=self.gen,
allowed_variants=self.allowed_variants,
variant_adptv=self.variant_adptv,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
seed=self.seed),
'cmaes':
dict(gen=self.gen,
cc=self.cc,
cs=self.cs,
c1=self.c1,
cmu=self.cmu,
sigma0=self.sigma0,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
force_bounds=self.force_bounds,
seed=self.seed),
'moead':
dict(gen=self.gen,
weight_generation=self.weight_generation,
decomposition=self.decomposition,
neighbours=self.neighbours,
CR=self.CR,
F=self.F,
eta_m=self.eta_m,
realb=self.realb,
limit=self.limit,
preserve_diversity=self.preserve_diversity,
seed=self.seed),
'compass_search':
dict(max_fevals=self.max_fevals,
start_range=self.start_range,
stop_range=self.stop_range,
reduction_coeff=self.reduction_coeff),
'simulated_annealing':
dict(Ts=self.Ts,
Tf=self.Tf,
n_T_adj=self.n_T_adj,
n_range_adj=self.n_range_adj,
bin_size=self.bin_size,
start_range=self.start_range,
seed=self.seed),
'pso':
dict(gen=self.gen,
omega=self.omega,
eta1=self.eta1,
eta2=self.eta2,
max_vel=self.max_vel,
variant=self.variant,
neighb_type=self.neighb_type,
neighb_param=self.neighb_param,
memory=self.memory,
seed=self.seed),
'pso_gen':
dict(gen=self.gen,
omega=self.omega,
eta1=self.eta1,
eta2=self.eta2,
max_vel=self.max_vel,
variant=self.variant,
neighb_type=self.neighb_type,
neighb_param=self.neighb_param,
memory=self.memory,
seed=self.seed),
'nsga2':
dict(gen=self.gen,
cr=self.cr,
eta_c=self.eta_c,
m=self.m,
eta_m=self.eta_m,
seed=self.seed),
'nspso':
dict(gen=self.gen,
omega=self.omega,
c1=self.c1,
c2=self.c2,
chi=self.chi,
v_coeff=self.v_coeff,
leader_selection_range=self.leader_selection_range,
diversity_mechanism=self.diversity_mechanism,
memory=self.memory,
seed=self.seed),
'mbh':
dict(algo=self.algo,
stop=self.stop,
perturb=self.perturb,
seed=self.seed),
'cstrs_self_adaptive':
dict(iters=self.iters, algo=self.algo, seed=self.seed),
'ihs':
dict(gen=self.gen,
phmcr=self.phmcr,
ppar_min=self.ppar_min,
ppar_max=self.ppar_max,
bw_min=self.bw_min,
bw_max=self.bw_max,
seed=self.seed),
'xnes':
dict(gen=self.gen,
eta_mu=self.eta_mu,
eta_sigma=self.eta_sigma,
eta_b=self.eta_b,
sigma0=self.sigma0,
ftol=self.ftol,
xtol=self.xtol,
memory=self.memory,
force_bounds=self.force_bounds,
seed=self.seed)
}
if self.log_data:
xl = []
yl = []
log_data = self.log_data
#
class interf_function:
def __init__(self, dim):
self.dim = dim
def fitness(self, x):
x = np.expand_dims(x, axis=0)
y = function(x)
# x = x[0]
y = y.tolist()
if log_data:
xl.append(x)
yl.append(y)
# print (x, y[0])
return y[0]
if function.is_differentiable():
def gradient(self, x):
x = np.expand_dims(x, axis=0)
g = function(x)
g = g.tolist()
return g[0]
def get_bounds(self):
lb = []
ub = []
bounds = function.get_ranges()
# warning
# check for infinities
for i in range(len(bounds)):
lb.append(bounds[i, 0])
ub.append(bounds[i, 1])
r = (np.array(lb), np.array(ub))
return r
# I need to call pygmo functions directly
prob = pg.problem(interf_function(function))
# print (prob.get_thread_safety())
if self.scanner == "sade":
# I need a dictionary with algorithms and options
algo = pg.algorithm(pg.sade(**scanner_options[self.scanner]))
elif self.scanner == "gaco":
algo = pg.algorithm(pg.gaco(**scanner_options[self.scanner]))
# elif self.scanner == "maco": # is not implemented though in webpage
# looks it is
# algo = pg.algorithm(pg.maco(**scanner_options[self.scanner]))
elif self.scanner == "gwo":
algo = pg.algorithm(pg.gwo(**scanner_options[self.scanner]))
elif self.scanner == "bee_colony":
algo = pg.algorithm(pg.bee_colony(**scanner_options[self.scanner]))
elif self.scanner == "de":
algo = pg.algorithm(pg.de(**scanner_options[self.scanner]))
elif self.scanner == "sea":
algo = pg.algorithm(pg.sea(**scanner_options[self.scanner]))
elif self.scanner == "sga":
algo = pg.algorithm(pg.sga(**scanner_options[self.scanner]))
elif self.scanner == "de1220":
algo = pg.algorithm(pg.de1220(**scanner_options[self.scanner]))
elif self.scanner == "cmaes":
algo = pg.algorithm(pg.cmaes(**scanner_options[self.scanner]))
# elif self.scanner == "moead": #multiobjective algorithm
# algo = pg.algorithm(pg.moead(**scanner_options[self.scanner]))
elif self.scanner == "compass_search":
algo = pg.algorithm(
pg.compass_search(**scanner_options[self.scanner]))
elif self.scanner == 'simulated_annealing':
algo = pg.algorithm(
pg.simulated_annealing(**scanner_options[self.scanner]))
elif self.scanner == 'pso':
algo = pg.algorithm(pg.pso(**scanner_options[self.scanner]))
elif self.scanner == 'pso_gen':
algo = pg.algorithm(pg.pso_gen(**scanner_options[self.scanner]))
# elif self.scanner == 'nsga2': #multiobjective algorithm
# algo = pg.algorithm(pg.nsga2(**scanner_options[self.scanner]))
# elif self.scanner == 'nspso': is not implemented though in webpage
# looks it is
# algo = pg.algorithm(pg.nspso(**scanner_options[self.scanner]))
elif self.scanner == 'mbh':
if scanner_options[self.scanner]['algo'] == 'de':
algo = pg.algorithm(
pg.mbh(pg.algorithm(pg.de(**scanner_options['de']))))
# elif self.scanner == 'ihs': #does not work
# algo = pg.algorithm(ihs(**scanner_options[self.scanner]))
# elif self.scanner == 'xnes': #does not work
# algo = pg.algorithm(xnes(**scanner_options[self.scanner]))
# uda = algo.extract(xnes)
else:
print(
'The ' + self.scanner + ' algorithm is not implemented. The '
'list of algorithms available is', algorithms)
sys.exit()
# add verbosing flag
if self.verbose > 1:
algo.set_verbosity(self.verbose)
pop = pg.population(prob, self.size)
if self.verbose > 9:
print('prob', prob)
opt = algo.evolve(pop)
if self.verbose > 9:
print('algo', algo)
# best_x = np.expand_dims(opt.champion_x, axis=0)
# best_fitness = np.expand_dims(opt.get_f()[opt.best_idx()], axis=0)
best_x = np.expand_dims(opt.champion_x, axis=0)
best_fitness = np.expand_dims(opt.champion_f, axis=0)
if self.verbose > 0:
print('best fit:', best_x, best_fitness)
if self.log_data:
x = np.squeeze(xl, axis=(1, ))
y = np.squeeze(yl, axis=(2, ))
if self.log_data:
return (x, y)
else:
return (best_x, best_fitness)
