def test_distribution_type(self):
"""Testing whether the distribution_type property works"""
# Ensure the default distribution type is point_to_point
a = archipelago()
self.assertEqual(a.distribution_type, distribution_type.point_to_point)
for dist_type in [distribution_type.point_to_point, distribution_type.broadcast]:
a = archipelago()
a.distribution_type = dist_type
self.assertEqual(a.distribution_type, dist_type)
def test_distribution_type(self):
"""Testing whether the distribution_type property works"""
# Ensure the default distribution type is point_to_point
a = archipelago()
self.assertEqual(a.distribution_type, distribution_type.point_to_point)
for dist_type in [
distribution_type.point_to_point, distribution_type.broadcast
]:
a = archipelago()
a.distribution_type = dist_type
self.assertEqual(a.distribution_type, dist_type)
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 do_test_migr_setup(self, pop_xs, out_pop_xs, top, n_evolves):
""" Generic procedure for testing whether the state of populations in 'pop_xs',
after performing 'n_evolve' migration steps is equal to the expected 'out_pop_xs', given topology 'top'. """
prob = problem.identity()
alg = algorithm.null()
pops = []
for xs in pop_xs:
pop = population(prob)
for x in xs:
pop.push_back(x)
pops.append(pop)
archi = archipelago(
distribution_type=distribution_type.broadcast,
migration_direction=migration_direction.destination)
for pop in pops:
archi.push_back(
island(alg,
pop,
s_policy=migration.best_s_policy(),
r_policy=migration.fair_r_policy()))
archi.topology = top
archi.evolve_batch(n_evolves, 1, False)
out_xs = []
for ii, isl in enumerate(archi, 1):
out_xs.append(tuple(sorted([i.cur_f for i in isl.population])))
out_xs = tuple(out_xs)
self.assertEqual(out_xs, out_pop_xs)
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_example5():
from PyGMO import archipelago, problem
from PyGMO.algorithm import jde
from PyGMO.topology import ring
from PyKEP import epoch
from PyKEP.planet import jpl_lp
from PyKEP.trajopt import mga_1dsm
# We define an Earth-Venus-Earth problem (single-objective)
seq = [jpl_lp('earth'), jpl_lp('venus'), jpl_lp('earth')]
prob = mga_1dsm(seq=seq)
prob.set_tof(0.7, 3)
prob.set_vinf(2.5)
prob.set_launch_window(epoch(5844), epoch(6209))
prob.set_tof(0.7, 3)
# We solve it!!
algo = jde(100)
topo = ring()
archi = archipelago(algo, prob, 8, 20, topology=topo)
print(
"Running a Self-Adaptive Differential Evolution Algorithm .... on 8 parallel islands"
)
archi.evolve(10)
archi.join()
isl = min(archi, key=lambda x: x.population.champion.f[0])
print("Done!! Best solution found is: " +
str(isl.population.champion.f[0] / 1000) + " km / sec")
prob.pretty(isl.population.champion.x)
prob.plot(isl.population.champion.x)
def __test_impl(self,isl_type,algo,prob): from PyGMO import archipelago, topology a = archipelago(topology = topology.ring()) for i in range(0,100): a.push_back(isl_type(algo,prob,6)) a.evolve(10) a.join()
def run_example5():
from PyGMO import archipelago, problem
from PyGMO.algorithm import jde
from PyGMO.topology import ring
from PyKEP import planet_ss,epoch
from PyKEP.interplanetary import mga_1dsm
#We define an Earth-Venus-Earth problem (single-objective)
seq = [planet_ss('earth'),planet_ss('venus'),planet_ss('earth')]
prob = mga_1dsm(seq=seq)
prob.set_tof(0.7,3)
prob.set_vinf(2.5)
prob.set_launch_window(epoch(5844),epoch(6209))
prob.set_tof(0.7,3)
print prob
#We solve it!!
algo = jde(100)
topo = ring()
archi = archipelago(algo,prob,8,20, topology=topo)
print "Running a Self-Adaptive Differential Evolution Algorithm .... on 8 parallel islands"
archi.evolve(10); archi.join()
isl = min(archi, key=lambda x:x.population.champion.f[0])
print "Done!! Best solution found is: " + str(isl.population.champion.f[0]/1000) + " km / sec"
prob.pretty(isl.population.champion.x)
prob.plot(isl.population.champion.x)
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_pickle(self):
from PyGMO import archipelago, island_list, problem_list, algorithm_list
import pickle
print('')
for isl in island_list:
for prob in problem_list:
for algo in algorithm_list:
print(isl,type(prob()),type(algo()))
a = archipelago()
a.push_back(isl(algo(),prob(),20))
a.push_back(isl(algo(),prob(),20))
pickle.loads(pickle.dumps(a))
def test_pickle(self):
from PyGMO import archipelago, island_list, problem_list, algorithm_list
import pickle
print('')
for isl in island_list:
for prob in problem_list:
for algo in algorithm_list:
print(isl, type(prob()), type(algo()))
a = archipelago()
a.push_back(isl(algo(), prob(), 20))
a.push_back(isl(algo(), prob(), 20))
pickle.loads(pickle.dumps(a))
def test_pickle(self):
from PyGMO import archipelago, island_list, problem_list, algorithm_list, problem
import pickle
from copy import deepcopy
# We remove some problems that cannot be constructed without external txt data files
prob_list = deepcopy(problem_list)
prob_list.remove(problem.cec2013)
print('')
for isl in island_list:
for prob in prob_list:
for algo in algorithm_list:
print(isl,type(prob()),type(algo()))
a = archipelago()
a.push_back(isl(algo(),prob(),20))
a.push_back(isl(algo(),prob(),20))
pickle.loads(pickle.dumps(a))
def _generate_solution(self, problem: Tuple[CoreProblem, SortedDict],
n_individuals: int, n_threads: int,
topol) -> Solution:
"""
Creates a new archipelago to solve the problem with the DE algorithm.
It returns the best solution found after the optimization process.
"""
archi = archipelago(self._algorithm,
problem[0],
n_threads,
n_individuals,
topology=topol)
archi.evolve(self._evol)
archi.join()
solution = self._get_best_solution(archi, problem)
return solution
def example_1(n_trials=25, variant_adptv=1, memory=True): from PyGMO import problem, algorithm, island, archipelago from PyGMO.topology import fully_connected from numpy import mean, median results = list() prob = problem.messenger_full() de_variants = [11,13,15,17] algos = [algorithm.jde(gen=50,variant=v, memory=memory, variant_adptv=variant_adptv) for v in de_variants] for trial in range(n_trials): archi = archipelago(topology=fully_connected()) for algo in algos: archi.push_back(island(algo,prob,25)) print "Trial N: " + str(trial) archi.evolve(30) results.append(min([isl.population.champion.f[0] for isl in archi])) return (mean(results), median(results), min(results), max(results))
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_pickle(self):
# We remove some problems that cannot be constructed without external
# txt data files
prob_list = deepcopy(problem_list)
prob_list.remove(problem.cec2013)
# Remove trajectory problems if PyKEP is not installed
try:
import PyKEP
except ImportError:
prob_list.remove(problem.py_pl2pl)
print('')
for isl in island_list:
for prob in prob_list:
for algo in algorithm_list:
a = archipelago()
a.push_back(isl(algo(), prob(), 20))
a.push_back(isl(algo(), prob(), 20))
pickle.loads(pickle.dumps(a))
def do_test_migr_setup(self, pop_xs, out_pop_xs, top, n_evolves):
""" Generic procedure for testing whether the state of populations in 'pop_xs',
after performing 'n_evolve' migration steps is equal to the expected 'out_pop_xs', given topology 'top'. """
prob = problem.identity()
alg = algorithm.null()
pops = []
for xs in pop_xs:
pop = population(prob)
for x in xs:
pop.push_back(x)
pops.append(pop)
archi = archipelago(distribution_type=distribution_type.broadcast, migration_direction=migration_direction.destination)
for pop in pops:
archi.push_back(island(alg, pop, s_policy=migration.best_s_policy(), r_policy=migration.fair_r_policy()))
archi.topology = top
archi.evolve_batch(n_evolves, 1, False)
out_xs = []
for ii, isl in enumerate(archi, 1):
out_xs.append(tuple(sorted([i.cur_f for i in isl.population])))
out_xs = tuple(out_xs)
self.assertEqual(out_xs, out_pop_xs)
def problem_solver(n_trials=25):
from PyGMO import problem, algorithm, island, archipelago
from PyGMO.topology import fully_connected
from numpy import mean, median
import csv
results = list()
prob = problem.cassini_2()
de_variants = [11, 13, 15, 17]
f_variants = [0, 0.2, 0.5, 0.8, 1]
cr_variants = [0.3, 0.9]
np_variants = [10, 25, 50]
for f in f_variants:
for cr in cr_variants:
for np in np_variants:
algos = [algorithm.de(gen=np, f=f, cr=cr) for v in de_variants]
for trial in range(n_trials):
archi = archipelago(topology=fully_connected())
for algo in algos:
archi.push_back(island(algo, prob, 25))
archi.evolve(30)
results.append(
min([isl.population.champion.f[0] for isl in archi]))
with open('results.csv', 'a') as out:
out.write('%f;' % f)
out.write('%f;' % cr)
out.write('%f;' % np)
out.write('\n')
out.write('%f;' % mean(results))
out.write('%f;' % median(results))
out.write('%f;' % min(results))
out.write('%f;' % max(results))
out.write('\n')
out.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 test_pickle(self):
from PyGMO import archipelago, island_list, problem_list, algorithm_list, problem
import pickle
from copy import deepcopy
# We remove some problems that cannot be constructed without external
# txt data files
prob_list = deepcopy(problem_list)
prob_list.remove(problem.cec2013)
# Remove trajectory problems if PyKEP is not installed
try:
import PyKEP
except ImportError:
prob_list.remove(problem.py_pl2pl)
print('')
for isl in island_list:
for prob in prob_list:
for algo in algorithm_list:
a = archipelago()
a.push_back(isl(algo(), prob(), 20))
a.push_back(isl(algo(), prob(), 20))
pickle.loads(pickle.dumps(a))
def problem_solver(n_trials=25):
from PyGMO import problem, algorithm, island, archipelago
from PyGMO.topology import fully_connected
from numpy import mean, median
import csv
results = list()
prob = problem.cassini_2()
de_variants = [11,13,15,17]
f_variants = [0, 0.2, 0.5, 0.8, 1]
cr_variants = [0.3, 0.9]
np_variants = [10, 25, 50]
for f in f_variants:
for cr in cr_variants:
for np in np_variants:
algos = [algorithm.de(gen=np, f=f, cr=cr) for v in de_variants]
for trial in range(n_trials):
archi = archipelago(topology=fully_connected())
for algo in algos:
archi.push_back(island(algo,prob,25))
archi.evolve(30)
results.append(min([isl.population.champion.f[0] for isl in archi]))
with open('results.csv', 'a') as out:
out.write('%f;' % f)
out.write('%f;' % cr)
out.write('%f;' % np)
out.write('\n')
out.write('%f;' % mean(results))
out.write('%f;' % median(results))
out.write('%f;' % min(results))
out.write('%f;' % max(results))
out.write('\n')
out.close()
