예제 #1
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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))
예제 #2
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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)))