def run_test():
from PyGMO import problem, algorithm, island
from numpy import mean, std
number_of_trials = 200
number_of_individuals = 20
number_of_generations = 500
prob_list = [problem.schwefel(10), problem.rastrigin(10), problem.rosenbrock(10), problem.ackley(10), problem.griewank(10)]
algo_list = [algorithm.pso(number_of_generations), algorithm.de(number_of_generations,0.8,0.8,2),algorithm.sa_corana(number_of_generations*number_of_individuals,1,0.1), algorithm.ihs(number_of_generations*number_of_individuals), algorithm.sga(number_of_generations,0.8,0.1)]
for prob in prob_list:
print('\nTesting problem: ' + str(type(prob)) + ', Dimension: ' + str(prob.dimension))
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_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 run_test():
from PyGMO import problem, algorithm, island
from numpy import mean, std
number_of_trials = 200
number_of_individuals = 20
number_of_generations = 500
prob_list = [
problem.schwefel(10),
problem.rastrigin(10),
problem.rosenbrock(10),
problem.ackley(10),
problem.griewank(10)
]
algo_list = [
algorithm.pso(number_of_generations),
algorithm.de(number_of_generations, 0.8, 0.8, 2),
algorithm.sa_corana(number_of_generations * number_of_individuals, 1,
0.1),
algorithm.ihs(number_of_generations * number_of_individuals),
algorithm.sga(number_of_generations, 0.8, 0.1)
]
for prob in prob_list:
print('\nTesting problem: ' + str(type(prob)) + ', Dimension: ' +
str(prob.dimension))
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)))