示例#1
0
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()
示例#2
0
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 test_simple_probs(self):
        """ Testing whether migration history matches the expected output for each migration_direction and distribution_type """

        for migr_dir in [
                migration_direction.source, migration_direction.destination
        ]:
            for dist_type in [
                    distribution_type.point_to_point,
                    distribution_type.broadcast
            ]:
                prob = problem.rosenbrock(10)
                alg = algorithm.jde(20)
                archi = archipelago(alg,
                                    prob,
                                    3,
                                    20,
                                    migration_direction=migr_dir,
                                    distribution_type=dist_type)
                top = topology.ring(3)
                top.set_weight(0, 1, 0.0)
                top.set_weight(0, 2, 0.0)
                top.set_weight(1, 2, 0.0)
                top.set_weight(1, 0, 1.0)
                top.set_weight(2, 0, 1.0)
                top.set_weight(2, 1, 1.0)
                archi.topology = top
                archi.evolve(200)
                migr_hist = archi.dump_migr_history()
                # Below: After 200 evaluations, there should be some migrants from 1->0, 2->0 and 2->1
                # There should be no migrants from 1->0, 2->0 and 2->1
                self.assertTrue("(1,0,1)" not in migr_hist)
                self.assertTrue("(1,0,2)" not in migr_hist)
                self.assertTrue("(1,1,2)" not in migr_hist)
示例#4
0
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)))
示例#5
0
    def test_topology_serialize(self):
        """ Testing whether the weights are retained after serialization of an archipelago """

        prob = problem.rosenbrock(10)
        alg = algorithm.jde(20)
        archi = archipelago(alg, prob, 4, 20)
        top = topology.ring(4)
        top.set_weight(0, 1, 0.01)
        top.set_weight(0, 3, 0.03)
        top.set_weight(1, 2, 0.12)
        top.set_weight(2, 1, 0.21)
        archi.topology = top
        archi.evolve(5)
        import pickle
        pickle.loads(pickle.dumps(archi))
        self.assertEqual(archi.topology.get_weight(0, 1), 0.01)
        self.assertEqual(archi.topology.get_weight(0, 3), 0.03)
        self.assertEqual(archi.topology.get_weight(1, 2), 0.12)
        self.assertEqual(archi.topology.get_weight(2, 1), 0.21)
示例#6
0
    def test_topology_serialize(self):
        """ Testing whether the weights are retained after serialization of an archipelago """

        prob = problem.rosenbrock(10)
        alg = algorithm.jde(20)
        archi = archipelago(alg, prob, 4, 20)
        top = topology.ring(4)
        top.set_weight(0, 1, 0.01)
        top.set_weight(0, 3, 0.03)
        top.set_weight(1, 2, 0.12)
        top.set_weight(2, 1, 0.21)
        archi.topology = top
        archi.evolve(5)
        import pickle
        pickle.loads(pickle.dumps(archi))
        self.assertEqual(archi.topology.get_weight(0, 1), 0.01)
        self.assertEqual(archi.topology.get_weight(0, 3), 0.03)
        self.assertEqual(archi.topology.get_weight(1, 2), 0.12)
        self.assertEqual(archi.topology.get_weight(2, 1), 0.21)
示例#7
0
    def test_simple_probs(self):
        """ Testing whether migration history matches the expected output for each migration_direction and distribution_type """

        for migr_dir in [migration_direction.source, migration_direction.destination]:
            for dist_type in [distribution_type.point_to_point, distribution_type.broadcast]:
                prob = problem.rosenbrock(10)
                alg = algorithm.jde(20)
                archi = archipelago(alg, prob, 3, 20, migration_direction=migr_dir, distribution_type=dist_type)
                top = topology.ring(3)
                top.set_weight(0, 1, 0.0)
                top.set_weight(0, 2, 0.0)
                top.set_weight(1, 2, 0.0)
                top.set_weight(1, 0, 1.0)
                top.set_weight(2, 0, 1.0)
                top.set_weight(2, 1, 1.0)
                archi.topology = top
                archi.evolve(200)
                migr_hist = archi.dump_migr_history()
                # Below: After 200 evaluations, there should be some migrants from 1->0, 2->0 and 2->1
                # There should be no migrants from 1->0, 2->0 and 2->1
                self.assertTrue("(1,0,1)" not in migr_hist)
                self.assertTrue("(1,0,2)" not in migr_hist)
                self.assertTrue("(1,1,2)" not in migr_hist)
示例#8
0
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)))
示例#9
0
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)))