def main():
# instantiate dynamics
dyn = Dynamics()
# instantiate leg
leg = Leg(dyn, alpha=0, bound=True)
# set boudaries
t0 = 0
s0 = np.array([1000, 1000, *np.random.randn(4)], dtype=float)
l0 = np.random.randn(len(s0))
tf = 1000
sf = np.zeros(len(s0))
leg.set(t0, s0, l0, tf, sf)
# define problem
udp = Problem(leg, atol=1e-8, rtol=1e-8)
prob = pg.problem(udp)
# instantiate algorithm
uda = pg7.snopt7(True, "/usr/lib/libsnopt7_c.so")
uda.set_integer_option("Major iterations limit", 4000)
uda.set_integer_option("Iterations limit", 40000)
uda.set_numeric_option("Major optimality tolerance", 1e-2)
uda.set_numeric_option("Major feasibility tolerance", 1e-4)
algo = pg.algorithm(uda)
# instantiate population with one chromosome
pop = pg.population(prob, 1)
#pop = pg.population(prob, 0)
#pop.push_back([1000, *l0])
# optimise
pop = algo.evolve(pop)
def algo_factory(name, original_screen_output=True):
if name == "slsqp":
uda = pg.nlopt('slsqp')
uda.xtol_rel = 1e-5
uda.ftol_rel = 0
algo = pg.algorithm(uda)
algo.set_verbosity(1)
return algo
elif name == "ipopt":
if original_screen_output:
pl = 5
else:
pl = 0
# Disable lint check on next line. Known issue (pagmo2/issues/261)
uda = pg.ipopt() # pylint: disable=no-member
uda.set_integer_option("print_level", pl)
uda.set_integer_option("acceptable_iter", 4)
uda.set_integer_option("max_iter", 150)
uda.set_numeric_option("tol", 1e-8)
uda.set_numeric_option("dual_inf_tol", 1e-8)
uda.set_numeric_option("constr_viol_tol", 1e-8)
uda.set_numeric_option("compl_inf_tol", 1e-8)
uda.set_numeric_option("acceptable_tol", 1e-3)
uda.set_numeric_option("acceptable_dual_inf_tol", 1e-2)
uda.set_numeric_option("acceptable_constr_viol_tol", 1e-6)
uda.set_numeric_option("acceptable_compl_inf_tol", 1e-6)
algo = pg.algorithm(uda)
return algo
elif name == "snopt7":
import pygmo_plugins_nonfree as pg7
uda = pg7.snopt7(original_screen_output,
"/usr/local/lib/libsnopt7_c.so")
uda.set_integer_option("Major iterations limit", 2000)
uda.set_integer_option("Iterations limit", 200000)
uda.set_numeric_option("Major optimality tolerance", 1e-2)
uda.set_numeric_option("Major feasibility tolerance", 1e-9)
algo = pg.algorithm(uda)
return algo
def algo_factory(name, original_screen_output=True):
if name is "slsqp":
uda = pg.nlopt('slsqp')
uda.xtol_rel = 1e-5
uda.ftol_rel = 0
algo = pg.algorithm(uda)
algo.set_verbosity(1)
return algo
elif name is "ipopt":
if original_screen_output:
pl = 5
else:
pl = 0
uda = pg.ipopt()
uda.set_integer_option("print_level", pl)
uda.set_integer_option("acceptable_iter", 4)
uda.set_integer_option("max_iter", 150)
uda.set_numeric_option("tol", 1e-8)
uda.set_numeric_option("dual_inf_tol", 1e-8)
uda.set_numeric_option("constr_viol_tol", 1e-8)
uda.set_numeric_option("compl_inf_tol", 1e-8)
uda.set_numeric_option("acceptable_tol", 1e-3)
uda.set_numeric_option("acceptable_dual_inf_tol", 1e-2)
uda.set_numeric_option("acceptable_constr_viol_tol", 1e-6)
uda.set_numeric_option("acceptable_compl_inf_tol", 1e-6)
algo = pg.algorithm(uda)
return algo
elif name is "snopt7":
import pygmo_plugins_nonfree as pg7
uda = pg7.snopt7(original_screen_output,
"/usr/local/lib/libsnopt7_c.so")
uda.set_integer_option("Major iterations limit", 2000)
uda.set_integer_option("Iterations limit", 200000)
uda.set_numeric_option("Major optimality tolerance", 1e-2)
uda.set_numeric_option("Major feasibility tolerance", 1e-9)
algo = pg.algorithm(uda)
return algo
import pygmo as pg import pygmo_plugins_nonfree as ppn # 1 - Instantiate a pygmo problem constructing it from a UDP # (user defined problem). prob = pg.problem(pg.schwefel(30)) # 2 - Instantiate a pagmo_plugins_nonfree algorithm, in this case SNOPT. # Here we assume the library name is libsnopt7_c.so, in Windows it would probably be # snopt7_c.dll or similar. algo = pg.algorithm(ppn.snopt7(false, "/usr/local/lib/libsnopt7_c.so")) # 3 - Instantiate an archipelago with 16 islands having each 20 individuals archi = pg.archipelago(16, algo=algo, prob=prob, pop_size=20) # 4 - Run the evolution in parallel on the 16 separate islands 10 times. archi.evolve(10) # 5 - Wait for the evolutions to be finished archi.wait() # 6 - Print the fitness of the best solution in each island res = [isl.get_population().champion_f for isl in archi] print(res)