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 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 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 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)))
