def run_pygmo():
algos = [('PyGMO_DE',algorithm.de(gen=1)),
('PyGMO_DE_v1',algorithm.de(gen=1,f=.2,cr=1.0,variant=4)),
#('PyGMO_DE_v2',algorithm.de(gen=1,f=.1,cr=1.0,variant=4)),
#('PyGMO_DE_v3',algorithm.de(gen=1,f=.3,cr=1.0,variant=4)),
#('PyGMO_DE_v4',algorithm.de(gen=1,f=.2,cr=0.9,variant=4)),
('PyGMO_JDE',algorithm.jde(gen=1)),
('PyGMO_MDE_PBX',algorithm.mde_pbx(gen=1)),
('PyGMO_PSO',algorithm.pso(gen=1,variant=5)),
('PyGMO_PSO_GEN',algorithm.pso_gen(gen=1)),
('PyGMO_BEE_COLONY',algorithm.bee_colony(gen=1)),
('PyGMO_IHS',algorithm.ihs(iter=pop_size))]
run_eval("PyGMO",algos)
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 run(n_tri,n_gen,pop_size):
import numpy
#inital number of generations
number_of_generations = 1
number_of_trials = n_tri
number_of_individuals = pop_size
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)
]
algo_means_list = []
algo_name_list = []
print('\nTrials: ' + str(n_tri) + ' - Population size: ' + str(pop_size) + ' - Generations: ' + str(n_gen))
for algo in algo_list:
print(' ' + str(algo))
algo_name_list.append(algo.get_name())
trials = []
trials_max_len = 0
for i in range(number_of_trials):
print ".",
trials.append(single_test(algo,total_evals,pop_size))
t_minlen = min([len(t) for t in trials])
t_new = numpy.array([t[:t_minlen-1] for t in trials ])
t_new = numpy.mean(t_new,axis=0)
algo_means_list.append(t_new[:,1])
plot_multi(algo_means_list,names=algo_name_list,trials=number_of_trials)
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)))
