def avaliacao(self, populacao):
n = len(populacao)
def steps(k):
individuo = populacao[k, :]
obj = self.funcao_objetivo(individuo)
return obj
ncpu = cpu_count()
pool = ProcessPool(nodes=ncpu)
pesos = array(pool.map(steps, range(n)))
pool.close()
pool.join()
pool.clear()
shutdown()
return pesos
assert res == std
def test_processing():
from pathos.pools import ProcessPool as MPP
pool = MPP()
res = timed_pool(pool, items, delay, verbose)
assert res == std
def test_threading():
from pathos.pools import ThreadPool as MTP
pool = MTP()
res = timed_pool(pool, items, delay, verbose)
assert res == std
if __name__ == '__main__':
if verbose:
print("CONFIG: delay = %s" % delay)
print("CONFIG: items = %s" % items)
print("")
from pathos.helpers import freeze_support, shutdown
freeze_support()
test_serial()
test_pp()
test_processing()
test_threading()
shutdown()
from pathos.pools import ParallelPool as Pool
solver = DifferentialEvolutionSolver2(ND, NP)
solver.SetMapper(Pool().map)
solver.SetRandomInitialPoints(min=[-100.0] * ND, max=[100.0] * ND)
solver.SetEvaluationLimits(generations=MAX_GENERATIONS)
solver.SetGenerationMonitor(VerboseMonitor(30))
solver.enable_signal_handler()
strategy = Best1Exp
#strategy = Best1Bin
solver.Solve(ChebyshevCost, termination=VTR(0.01), strategy=strategy, \
CrossProbability=1.0, ScalingFactor=0.9 , \
sigint_callback=plot_solution)
solution = solver.Solution()
return solution
if __name__ == '__main__':
from pathos.helpers import freeze_support, shutdown
freeze_support() # help Windows use multiprocessing
solution = main()
shutdown() # help multiprocessing shutdown all workers
print_solution(solution)
plot_solution(solution)
# end of file
