def example_2(
algo=algorithm.de(1),
prob=problem.rosenbrock(10),
topo=topology.barabasi_albert(
3,
3),
n_evolve=100,
n_isl=1024,
pop_size=20,
color_code='rank'):
from PyGMO import problem, algorithm, island, archipelago
from matplotlib.pyplot import savefig, close
archi = archipelago(algo, prob, n_isl, pop_size, topology=topo)
print("Drawing Initial Condition .. ")
pos = archi.draw(
scale_by_degree=True, n_size=3, e_alpha=0.03, n_color=color_code)
savefig('archi000', dpi=72)
close()
for i in range(1, n_evolve):
archi.evolve(1)
archi.join()
print("Drawing" + str(i) + "-th evolution .. ")
pos = archi.draw(
layout=pos,
scale_by_degree=True,
n_size=3,
e_alpha=0.03,
n_color=color_code)
savefig('archi%03d' % i, dpi=72)
close()
def example_2(
algo=algorithm.de(1),
prob=problem.rosenbrock(10),
topo=topology.barabasi_albert(
3,
3),
n_evolve=100,
n_isl=1024,
pop_size=20,
color_code='rank'):
from PyGMO import problem, algorithm, island, archipelago
from matplotlib.pyplot import savefig, close
archi = archipelago(algo, prob, n_isl, pop_size, topology=topo)
print("Drawing Initial Condition .. ")
pos = archi.draw(
scale_by_degree=True, n_size=3, e_alpha=0.03, n_color=color_code)
savefig('archi000', dpi=72)
close()
for i in range(1, n_evolve):
archi.evolve(1)
archi.join()
print("Drawing" + str(i) + "-th evolution .. ")
pos = archi.draw(
layout=pos,
scale_by_degree=True,
n_size=3,
e_alpha=0.03,
n_color=color_code)
savefig('archi%03d' % i, dpi=72)
close()
def test_simple_probs(self):
""" Testing whether migration history matches the expected output for each migration_direction and distribution_type """
for migr_dir in [
migration_direction.source, migration_direction.destination
]:
for dist_type in [
distribution_type.point_to_point,
distribution_type.broadcast
]:
prob = problem.rosenbrock(10)
alg = algorithm.jde(20)
archi = archipelago(alg,
prob,
3,
20,
migration_direction=migr_dir,
distribution_type=dist_type)
top = topology.ring(3)
top.set_weight(0, 1, 0.0)
top.set_weight(0, 2, 0.0)
top.set_weight(1, 2, 0.0)
top.set_weight(1, 0, 1.0)
top.set_weight(2, 0, 1.0)
top.set_weight(2, 1, 1.0)
archi.topology = top
archi.evolve(200)
migr_hist = archi.dump_migr_history()
# Below: After 200 evaluations, there should be some migrants from 1->0, 2->0 and 2->1
# There should be no migrants from 1->0, 2->0 and 2->1
self.assertTrue("(1,0,1)" not in migr_hist)
self.assertTrue("(1,0,2)" not in migr_hist)
self.assertTrue("(1,1,2)" not in migr_hist)
def run_test(n_trials=200, pop_size = 20, n_gen = 500):
number_of_trials = n_trials
number_of_individuals = pop_size
number_of_generations = n_gen
prob_list = [problem.schwefel(dim = 10),
problem.michalewicz(dim = 10),
problem.rastrigin(dim = 10),
problem.rosenbrock(dim = 10),
problem.ackley(dim = 10),
problem.griewank(dim = 10)]
if __extensions__['gtop']:
prob_list.append(problem.cassini_1())
prob_list.append(problem.cassini_2())
prob_list.append(problem.gtoc_1())
prob_list.append(problem.rosetta())
prob_list.append(problem.messenger_full())
prob_list.append(problem.tandem(prob_id = 6, max_tof = 10))
algo_list = [algorithm.pso(gen = number_of_generations),
algorithm.de(gen = number_of_generations,xtol=1e-30, ftol=1e-30),
algorithm.jde(gen = number_of_generations, variant_adptv=2,xtol=1e-30, ftol=1e-30),
algorithm.de_1220(gen = number_of_generations, variant_adptv=2,xtol=1e-30, ftol=1e-30),
algorithm.sa_corana(iter = number_of_generations*number_of_individuals,Ts = 1,Tf = 0.01),
algorithm.ihs(iter = number_of_generations*number_of_individuals),
algorithm.sga(gen = number_of_generations),
algorithm.cmaes(gen = number_of_generations,xtol=1e-30, ftol=1e-30),
algorithm.bee_colony(gen = number_of_generations/2)]
print('\nTrials: ' + str(n_trials) + ' - Population size: ' + str(pop_size) + ' - Generations: ' + str(n_gen))
for prob in prob_list:
print('\nTesting problem: ' + prob.get_name() + ', Dimension: ' + str(prob.dimension) )
print('With Population Size: ' + str(pop_size) )
for algo in algo_list:
print(' ' + str(algo))
best = []
best_x = []
for i in range(0,number_of_trials):
isl = island(algo,prob,number_of_individuals)
isl.evolve(1)
isl.join()
best.append(isl.population.champion.f)
best_x.append(isl.population.champion.x)
print(' Best:\t' + str(min(best)[0]))
print(' Mean:\t' + str(mean(best)))
print(' Std:\t' + str(std(best)))
def test_topology_serialize(self):
""" Testing whether the weights are retained after serialization of an archipelago """
prob = problem.rosenbrock(10)
alg = algorithm.jde(20)
archi = archipelago(alg, prob, 4, 20)
top = topology.ring(4)
top.set_weight(0, 1, 0.01)
top.set_weight(0, 3, 0.03)
top.set_weight(1, 2, 0.12)
top.set_weight(2, 1, 0.21)
archi.topology = top
archi.evolve(5)
import pickle
pickle.loads(pickle.dumps(archi))
self.assertEqual(archi.topology.get_weight(0, 1), 0.01)
self.assertEqual(archi.topology.get_weight(0, 3), 0.03)
self.assertEqual(archi.topology.get_weight(1, 2), 0.12)
self.assertEqual(archi.topology.get_weight(2, 1), 0.21)
def test_topology_serialize(self):
""" Testing whether the weights are retained after serialization of an archipelago """
prob = problem.rosenbrock(10)
alg = algorithm.jde(20)
archi = archipelago(alg, prob, 4, 20)
top = topology.ring(4)
top.set_weight(0, 1, 0.01)
top.set_weight(0, 3, 0.03)
top.set_weight(1, 2, 0.12)
top.set_weight(2, 1, 0.21)
archi.topology = top
archi.evolve(5)
import pickle
pickle.loads(pickle.dumps(archi))
self.assertEqual(archi.topology.get_weight(0, 1), 0.01)
self.assertEqual(archi.topology.get_weight(0, 3), 0.03)
self.assertEqual(archi.topology.get_weight(1, 2), 0.12)
self.assertEqual(archi.topology.get_weight(2, 1), 0.21)
def test_simple_probs(self):
""" Testing whether migration history matches the expected output for each migration_direction and distribution_type """
for migr_dir in [migration_direction.source, migration_direction.destination]:
for dist_type in [distribution_type.point_to_point, distribution_type.broadcast]:
prob = problem.rosenbrock(10)
alg = algorithm.jde(20)
archi = archipelago(alg, prob, 3, 20, migration_direction=migr_dir, distribution_type=dist_type)
top = topology.ring(3)
top.set_weight(0, 1, 0.0)
top.set_weight(0, 2, 0.0)
top.set_weight(1, 2, 0.0)
top.set_weight(1, 0, 1.0)
top.set_weight(2, 0, 1.0)
top.set_weight(2, 1, 1.0)
archi.topology = top
archi.evolve(200)
migr_hist = archi.dump_migr_history()
# Below: After 200 evaluations, there should be some migrants from 1->0, 2->0 and 2->1
# There should be no migrants from 1->0, 2->0 and 2->1
self.assertTrue("(1,0,1)" not in migr_hist)
self.assertTrue("(1,0,2)" not in migr_hist)
self.assertTrue("(1,1,2)" not in migr_hist)
def run_test(n_trials=200, pop_size=20, n_gen=500):
"""
This function runs some tests on the algorthm. Use it to verify the correct installation
of PyGMO.
USAGE: PyGMO.run_test(n_trials=200, pop_size = 20, n_gen = 500)
* n_trials: each algorithm will be called n_trials times on the same problem to then evaluate best, mean and std
* pop_size: this determines the population size
* n_gen: this regulates the maximim number of function evaluation
"""
from PyGMO import problem, algorithm, island
from numpy import mean, std
number_of_trials = n_trials
number_of_individuals = pop_size
number_of_generations = n_gen
prob_list = [
problem.schwefel(
dim=10), problem.rastrigin(
dim=10), problem.rosenbrock(
dim=10), problem.ackley(
dim=10), problem.griewank(
dim=10), problem.levy5(10)]
if __extensions__['gtop']:
prob_list.append(problem.cassini_1())
prob_list.append(problem.gtoc_1())
prob_list.append(problem.cassini_2())
prob_list.append(problem.messenger_full())
algo_list = [
algorithm.pso(
gen=number_of_generations),
algorithm.mde_pbx(
gen=number_of_generations,
xtol=1e-30,
ftol=1e-30),
algorithm.de(
gen=number_of_generations,
xtol=1e-30,
ftol=1e-30),
algorithm.jde(
gen=number_of_generations,
memory=False,
xtol=1e-30,
ftol=1e-30),
algorithm.de_1220(
gen=number_of_generations,
memory=False,
xtol=1e-30,
ftol=1e-30),
algorithm.sa_corana(
iter=number_of_generations *
number_of_individuals,
Ts=1,
Tf=0.01),
algorithm.ihs(
iter=number_of_generations *
number_of_individuals),
algorithm.sga(
gen=number_of_generations),
algorithm.cmaes(
gen=number_of_generations,
xtol=1e-30,
ftol=1e-30,
memory=False),
algorithm.bee_colony(
gen=number_of_generations /
2)]
print('\nTrials: ' + str(n_trials) + ' - Population size: ' +
str(pop_size) + ' - Generations: ' + str(n_gen))
for prob in prob_list:
print('\nTesting problem: ' + prob.get_name() +
', Dimension: ' + str(prob.dimension))
print('With Population Size: ' + str(pop_size))
for algo in algo_list:
print(' ' + str(algo))
best = []
best_x = []
for i in range(0, number_of_trials):
isl = island(algo, prob, number_of_individuals)
isl.evolve(1)
isl.join()
best.append(isl.population.champion.f)
best_x.append(isl.population.champion.x)
print(' Best:\t' + str(min(best)[0]))
print(' Mean:\t' + str(mean(best)))
print(' Std:\t' + str(std(best)))
def run_test(n_trials=200, pop_size=20, n_gen=500):
"""
This function runs some tests on the algorthm. Use it to verify the correct installation
of PyGMO.
USAGE: PyGMO.run_test(n_trials=200, pop_size = 20, n_gen = 500)
* n_trials: each algorithm will be called n_trials times on the same problem to then evaluate best, mean and std
* pop_size: this determines the population size
* n_gen: this regulates the maximim number of function evaluation
"""
from PyGMO import problem, algorithm, island
from numpy import mean, std
number_of_trials = n_trials
number_of_individuals = pop_size
number_of_generations = n_gen
prob_list = [
problem.schwefel(
dim=10), problem.rastrigin(
dim=10), problem.rosenbrock(
dim=10), problem.ackley(
dim=10), problem.griewank(
dim=10), problem.levy5(10)]
if __extensions__['gtop']:
prob_list.append(problem.cassini_1())
prob_list.append(problem.gtoc_1())
prob_list.append(problem.cassini_2())
prob_list.append(problem.messenger_full())
algo_list = [
algorithm.pso(
gen=number_of_generations),
algorithm.mde_pbx(
gen=number_of_generations,
xtol=1e-30,
ftol=1e-30),
algorithm.de(
gen=number_of_generations,
xtol=1e-30,
ftol=1e-30),
algorithm.jde(
gen=number_of_generations,
memory=False,
xtol=1e-30,
ftol=1e-30),
algorithm.de_1220(
gen=number_of_generations,
memory=False,
xtol=1e-30,
ftol=1e-30),
algorithm.sa_corana(
iter=number_of_generations *
number_of_individuals,
Ts=1,
Tf=0.01),
algorithm.ihs(
iter=number_of_generations *
number_of_individuals),
algorithm.sga(
gen=number_of_generations),
algorithm.cmaes(
gen=number_of_generations,
xtol=1e-30,
ftol=1e-30,
memory=False),
algorithm.bee_colony(
gen=number_of_generations /
2)]
print('\nTrials: ' + str(n_trials) + ' - Population size: ' +
str(pop_size) + ' - Generations: ' + str(n_gen))
for prob in prob_list:
print('\nTesting problem: ' + prob.get_name() +
', Dimension: ' + str(prob.dimension))
print('With Population Size: ' + str(pop_size))
for algo in algo_list:
print(' ' + str(algo))
best = []
best_x = []
for i in range(0, number_of_trials):
isl = island(algo, prob, number_of_individuals)
isl.evolve(1)
isl.join()
best.append(isl.population.champion.f)
best_x.append(isl.population.champion.x)
print(' Best:\t' + str(min(best)[0]))
print(' Mean:\t' + str(mean(best)))
print(' Std:\t' + str(std(best)))