def _run_inner(prefix, test_name, runs, g, c, args1, args2, args3, args4,
args5, args6, args7, args8, args9):
runner = TSPSRunner(g)
add_solver(runner, args1, c, runs)
add_solver(runner, args2, c, runs)
add_solver(runner, args3, c, runs)
add_solver(runner, args4, c, runs)
add_solver(runner, args5, c, runs)
add_solver(runner, args6, c, runs)
add_solver(runner, args7, c, runs)
add_solver(runner, args8, c, runs)
add_solver(runner, args9, c, runs)
runner.run()
fig = GenericFig(size=120)
result = TestRunResult(runner, fig, test_name + prefix, base_folder)
def plot():
result.averaged_x_values(args1.key, '-b')
result.averaged_x_values(args2.key, red_color(0))
result.averaged_x_values(args3.key, red_color(1))
result.averaged_x_values(args4.key, red_color(2))
result.averaged_x_values(args5.key, red_color(3))
result.averaged_x_values(args6.key, red_color(4))
result.averaged_x_values(args7.key, green_color(0))
result.averaged_x_values(args8.key, green_color(2))
result.averaged_x_values(args9.key, green_color(4))
result.next_subplot(test_name)
plot()
result.legend()
result.ylabel()
result.next_log_subplot(u"(log skaleje)")
plot()
result.save()
def run_base(cr):
prefix = " mr = {}".format(cr)
test_name = u"Rekombinacijos, mutacijos koef."
runs = 10
args1 = base_args(cr, 0.1)
args2 = base_args(cr, 0.2)
args3 = base_args(cr, 0.3)
args4 = base_args(cr, 0.4)
args5 = base_args(cr, 0.5)
args6 = base_args(cr, 0.6)
args7 = base_args(cr, 0.7)
args8 = base_args(cr, 0.8)
args9 = base_args(cr, 0.9)
r = runner()
add_solver(r, args1, c, runs)
add_solver(r, args2, c, runs)
add_solver(r, args3, c, runs)
add_solver(r, args4, c, runs)
add_solver(r, args5, c, runs)
add_solver(r, args6, c, runs)
add_solver(r, args7, c, runs)
add_solver(r, args8, c, runs)
add_solver(r, args9, c, runs)
r.run()
fig = GenericFig(size=120)
result = TestRunResult(r, fig, test_name + prefix, base_folder)
def plot():
result.averaged_x_values(args1.key, 'r--')
result.averaged_x_values(args2.key, red_color(0))
result.averaged_x_values(args3.key, red_color(2))
result.averaged_x_values(args4.key, red_color(4))
result.averaged_x_values(args5.key, green_color(4))
result.averaged_x_values(args6.key, green_color(2))
result.averaged_x_values(args7.key, green_color(1))
result.averaged_x_values(args8.key, green_color(0))
result.averaged_x_values(args9.key, 'g--')
result.next_subplot(test_name)
plot()
result.legend()
result.ylabel()
result.next_log_subplot(u"(log skaleje)")
plot()
result.save()
def run_0():
prefix = ""
test_name = u"Chromosomos"
runs = 10
args1 = base_args(10)
args2 = base_args(20)
args3 = base_args(30)
args4 = base_args(40)
args5 = base_args(50)
args6 = base_args(60)
args7 = base_args(70)
args8 = base_args(80)
r = runner()
add_solver(r, args1, c, runs)
add_solver(r, args2, c, runs)
add_solver(r, args3, c, runs)
add_solver(r, args4, c, runs)
add_solver(r, args5, c, runs)
add_solver(r, args6, c, runs)
add_solver(r, args7, c, runs)
add_solver(r, args8, c, runs)
r.run()
fig = GenericFig(size=120)
result = TestRunResult(r, fig, test_name + prefix, base_folder)
def plot():
result.averaged_x_values(args1.key, 'r--')
result.averaged_x_values(args2.key, red_color(0))
result.averaged_x_values(args3.key, red_color(2))
result.averaged_x_values(args4.key, red_color(4))
result.averaged_x_values(args5.key, green_color(4))
result.averaged_x_values(args6.key, green_color(2))
result.averaged_x_values(args7.key, green_color(0))
result.averaged_x_values(args8.key, 'g--')
result.next_subplot(test_name)
plot()
result.legend()
result.ylabel()
result.next_log_subplot(u"(log skaleje)")
plot()
result.save()
def run_base(chrom):
prefix = " chrom = {}".format(chrom)
test_name = u"Pradine populiacija"
runs = 10
args1 = base_args(chrom, int(chrom / 8.0 * 1))
args2 = base_args(chrom, int(chrom / 8.0 * 2))
args3 = base_args(chrom, int(chrom / 8.0 * 3))
args4 = base_args(chrom, int(chrom / 8.0 * 4))
args5 = base_args(chrom, int(chrom / 8.0 * 5))
args6 = base_args(chrom, int(chrom / 8.0 * 6))
args7 = base_args(chrom, int(chrom / 8.0 * 7))
args8 = base_args(chrom, int(chrom / 8.0 * 8))
r = runner()
add_solver(r, args1, c, runs)
add_solver(r, args2, c, runs)
add_solver(r, args3, c, runs)
add_solver(r, args4, c, runs)
add_solver(r, args5, c, runs)
add_solver(r, args6, c, runs)
add_solver(r, args7, c, runs)
add_solver(r, args8, c, runs)
r.run()
fig = GenericFig(size=120)
result = TestRunResult(r, fig, test_name + prefix, base_folder)
def plot():
result.averaged_x_values(args1.key, 'r--')
result.averaged_x_values(args2.key, red_color(0))
result.averaged_x_values(args3.key, red_color(2))
result.averaged_x_values(args4.key, red_color(4))
result.averaged_x_values(args5.key, green_color(4))
result.averaged_x_values(args6.key, green_color(2))
result.averaged_x_values(args7.key, green_color(0))
result.averaged_x_values(args8.key, 'g--')
result.next_subplot(test_name)
plot()
result.legend()
result.ylabel()
result.next_log_subplot(u"(log skaleje)")
plot()
result.save()
def run_base(cr):
prefix = " cr = {}".format(cr)
test_name = u"Rekombinacijos koef., pradinė populiacija"
runs = 10
args1 = base_args(cr, 6)
args2 = base_args(cr, 8)
args3 = base_args(cr, 10)
args4 = base_args(cr, 12)
args5 = base_args(cr, 14)
args6 = base_args(cr, 16)
r = runner()
add_solver(r, args1, c, runs)
add_solver(r, args2, c, runs)
add_solver(r, args3, c, runs)
add_solver(r, args4, c, runs)
add_solver(r, args5, c, runs)
add_solver(r, args6, c, runs)
r.run()
fig = GenericFig(size=120)
result = TestRunResult(r, fig, test_name + prefix, base_folder)
def plot():
result.averaged_x_values(args1.key, 'r--')
result.averaged_x_values(args2.key, red_color(1))
result.averaged_x_values(args3.key, red_color(4))
result.averaged_x_values(args4.key, green_color(4))
result.averaged_x_values(args5.key, green_color(1))
result.averaged_x_values(args6.key, 'g--')
result.next_subplot(test_name)
plot()
result.legend()
result.ylabel()
result.next_log_subplot(u"(log skaleje)")
plot()
result.save()
def _genetic_acs_test_type_1_(graph, runs, ants, chromosomes, iterations,
ant_iterations=1, subf="a"):
cities = len(graph.nodes())
filename = str(cities) + "_genetic_acs_test"
output_folder = base_folder + "1/" + subf
c1 = cond()
c1[CONDITION_GENERATION] = iterations
c2 = cond()
c2[CONDITION_GENERATION] = ant_iterations
ap = {
'beta': 30,
'alpha': 0.1,
'ro': 0.1,
'tau0': 10
}
ap1 = deepcopy(ap)
ap1['ants'] = ants
acs1 = AntColonySystem(graph, ap1, c2, absolute_mute=True)
ap2 = deepcopy(ap)
ap2['ants'] = int(ants / 2)
acs2 = AntColonySystem(graph, ap2, c2, absolute_mute=True)
gp = {
'chromosomes': chromosomes,
'cr': 0.1,
'mr': 0.5,
'crossover_method': CROSSOVER_OX,
'mutation_method': MUTATION_SWAP
}
gp11 = deepcopy(gp)
gp11['selection_method'] = SELECTION_ROULETE
gp11['use_acs'] = acs1
gp12 = deepcopy(gp)
gp12['selection_method'] = SELECTION_ROULETE
gp12['use_acs'] = acs2
gp13 = deepcopy(gp)
gp13['selection_method'] = SELECTION_ROULETE
gp21 = deepcopy(gp)
gp21['selection_method'] = SELECTION_RANK
gp21['use_acs'] = acs1
gp22 = deepcopy(gp)
gp22['selection_method'] = SELECTION_RANK
gp22['use_acs'] = acs2
gp23 = deepcopy(gp)
gp23['selection_method'] = SELECTION_RANK
runner = TSPSRunner(graph)
runner.add_tsps(Genetic, gp11, c1, runs, 'ga-acs-1.0-roulete')
runner.add_tsps(Genetic, gp12, c1, runs, 'ga-acs-0.5-roulete')
runner.add_tsps(Genetic, gp13, c1, runs, 'ga-roulete')
runner.add_tsps(Genetic, gp21, c1, runs, 'ga-acs-1.0-rank')
runner.add_tsps(Genetic, gp22, c1, runs, 'ga-acs-0.5-rank')
runner.add_tsps(Genetic, gp23, c1, runs, 'ga-rank')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("GA-ACS, miestu - " + str(cities) +
", ant-iteration = " + str(ant_iterations) +
",n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-roulete']
['all_distances'], line='r-',
label='roulete_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-roulete']
['all_distances'], line='r--',
label='roulete_ants_' + str(ants / 2))
fig.add_averaged_x_plot_values(runner.results['ga-roulete']
['all_distances'], line=':r',
label='roulete')
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-rank']
['all_distances'], line='g-',
label='rank_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-rank']
['all_distances'], line='g--',
label='rank_ants_' + str(ants / 2))
fig.add_averaged_x_plot_values(runner.results['ga-rank']
['all_distances'], line=':g',
label='rank')
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot()
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-roulete']
['all_distances'], line='r-',
label='roulete_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-roulete']
['all_distances'], line='r--',
label='roulete_ants_' + str(ants / 2))
fig.add_averaged_x_plot_values(runner.results['ga-roulete']
['all_distances'], line=':r',
label='roulete')
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-rank']
['all_distances'], line='g-',
label='rank_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-rank']
['all_distances'], line='g--',
label='rank_ants_' + str(ants / 2))
fig.add_averaged_x_plot_values(runner.results['ga-rank']
['all_distances'], line=':g',
label='rank')
fig.set_xscale('log')
fig.save(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("GA-ACS, miestu - " + str(cities) +
", ant-iteration = " + str(ant_iterations) +
",n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-roulete']
['all_distances'], line='r-',
label='roulete_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-roulete']
['all_distances'], line='r--',
label='roulete_ants_' + str(ants / 2))
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-rank']
['all_distances'], line='g-',
label='rank_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-rank']
['all_distances'], line='g--',
label='rank_ants_' + str(ants / 2))
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot()
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-roulete']
['all_distances'], line='r-',
label='roulete_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-roulete']
['all_distances'], line='r--',
label='roulete_ants_' + str(ants / 2))
fig.add_averaged_x_plot_values(runner.results['ga-acs-1.0-rank']
['all_distances'], line='g-',
label='rank_ants_' + str(ants))
fig.add_averaged_x_plot_values(runner.results['ga-acs-0.5-rank']
['all_distances'], line='g--',
label='rank_ants_' + str(ants / 2))
fig.set_xscale('log')
fig.save(filename=filename + "_b", output_folder=output_folder)
def _acs_test_type_1_(graph, runs, base_pars, beta, antz, iters, subf="a"):
cities = len(graph.nodes())
filename = str(cities) + "_acs_test_1__args[" + str(beta) + "][" + str(
base_pars['alpha']) + "][" + str(base_pars['ro']) + "]"
output_folder = base_folder + "1/" + subf
base_pars['beta'] = beta
c1 = cond()
c1[CONDITION_GENERATION] = iters
ap1 = deepcopy(base_pars)
ap1['ants'] = antz[0]
c2 = cond()
c2[CONDITION_GENERATION] = iters
ap2 = deepcopy(base_pars)
ap2['ants'] = antz[1]
runner = TSPSRunner(graph)
runner.add_tsps(AntColonySystem, ap1, c1, runs, 'acs-1')
runner.add_tsps(AntColonySystem, ap2, c2, runs, 'acs-2')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("ACS, miestu - " + str(cities) + ", n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['acs-1']['all_distances'],
line='r-',
label="ants = " + str(antz[0]))
fig.add_averaged_x_plot_values(runner.results['acs-2']['all_distances'],
line='g-',
label="ants = " + str(antz[1]))
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot("ACS, miestu - " + str(cities) + ", n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['acs-1']['all_distances'],
line='r-',
label="ants = " + str(antz[0]))
fig.add_averaged_x_plot_values(runner.results['acs-2']['all_distances'],
line='g-',
label="ants = " + str(antz[1]))
fig.set_xscale('log')
fig.save(filename=filename + "_a", output_folder=output_folder)
def _genetic_acs_sa_test_type_1_(graph,
runs,
iterations,
chromosomes,
mr,
selection,
subf="a"):
cities = len(graph.nodes())
filename = str(cities) + "_genetic_acs_sa_test_1_" + selection
output_folder = base_folder + "1/" + subf
c0 = cond()
c0[CONDITION_GENERATION] = iterations
g0 = g_pars(chromosomes, selection, mr)
c1, g1 = ga_pars(graph, iterations, chromosomes, selection, mr)
runner = TSPSRunner(graph)
runner.add_tsps(Genetic, g0, c0, runs, 'ga')
runner.add_tsps(Genetic, g1, c1, runs, 'ga-acs-sa')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("GA-ACS-SA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['ga']['all_distances'],
line='r-',
label='ga')
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa']['all_distances'],
line='g-',
label='ga-acs-sa')
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot("GA-ACS-SA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['ga']['all_distances'],
line='r-',
label='ga')
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa']['all_distances'],
line='g-',
label='ga-acs-sa')
fig.set_xscale('log')
fig.save(filename=filename, output_folder=output_folder)
def _genetic_acs_sa_test_type_2_(graph,
runs,
iterations,
chromosomes,
selection,
subf="a"):
cities = len(graph.nodes())
filename = str(cities) + "_genetic_acs_sa_test_1_" + selection
output_folder = base_folder + "2/" + subf
c1, g1 = ga_pars(graph, iterations, chromosomes, selection, 0.01)
c2, g2 = ga_pars(graph, iterations, chromosomes, selection, 0.5)
c3, g3 = ga_pars(graph, iterations, chromosomes, selection, 0.99)
runner = TSPSRunner(graph)
runner.add_tsps(Genetic, g1, c1, runs, 'ga-acs-sa1')
runner.add_tsps(Genetic, g2, c2, runs, 'ga-acs-sa2')
runner.add_tsps(Genetic, g3, c3, runs, 'ga-acs-sa3')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("GA-ACS-SA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa1']['all_distances'],
line='r-',
label='mr-0.01')
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa2']['all_distances'],
line='g-',
label='mr-0.5')
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa3']['all_distances'],
line='b-',
label='mr-0.99')
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot("GA-ACS-SA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa1']['all_distances'],
line='r-',
label='mr-0.01')
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa2']['all_distances'],
line='g-',
label='mr-0.5')
fig.add_averaged_x_plot_values(
runner.results['ga-acs-sa3']['all_distances'],
line='b-',
label='mr-0.99')
fig.set_xscale('log')
fig.save(filename=filename, output_folder=output_folder)
def _sa_test_type_2_(graph, runs, decrement, no_progress):
cities = len(graph.nodes())
filename = str(cities) + "_sa_test_sa-1_" + \
str(decrement) + "_no_progress_" + str(no_progress) + "_"
output_folder = base_folder + "2"
c = cond()
c[CONDITION_NO_PROGRESS] = no_progress
sp1 = {'initial_temp': 100, 'decrement': decrement}
runner = TSPSRunner(graph)
runner.add_tsps(SimulatedAnnealing, sp1, c, runs, 'sa-1')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
rez = 15
xs, ys, zs, maxs = sh_to_contour_heatmap_scatter(
runner.results['sa-1']['all_distances'], rez, True)
fig = GenericFig()
fig.next_subplot("SA, miestu - " + str(cities) + ", alpha = " +
str(decrement) + ", n = " + str(runs))
fig.add_scatter_heatmap(xs, ys, zs, rez)
fig.set_xlim(0, maxs[0])
fig.save(filename=filename + "_a", output_folder=output_folder)
fig = GenericFig()
fig.next_subplot("SA, miestu - " + str(cities) + ", alpha = " +
str(decrement) + ", n = " + str(runs))
fig.add_contour_heatmap(xs, ys, zs, rez)
fig.save(filename=filename + "_b", output_folder=output_folder)
def _sa_test_type_1_(graph, runs):
cities = len(graph.nodes())
filename = str(cities) + "_sa_test_sa-1"
output_folder = base_folder + "1"
c = cond()
sp1 = {'initial_temp': 100, 'decrement': 0.99}
sp2 = {'initial_temp': 100, 'decrement': 0.9999}
runner = TSPSRunner(graph)
runner.add_tsps(SimulatedAnnealing, sp1, c, runs, 'sa-1')
runner.add_tsps(SimulatedAnnealing, sp2, c, runs, 'sa-2')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(size=120)
fig.next_subplot("SA, miestu - " + str(cities) + ", n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['sa-1']['all_distances'],
line='r-',
label='decrement-0.99')
fig.add_averaged_x_plot_values(runner.results['sa-2']['all_distances'],
line='g-',
label='decrement-0.9999')
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot("GA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['sa-1']['all_distances'],
line='r-',
label='decrement-0.99')
fig.add_averaged_x_plot_values(runner.results['sa-2']['all_distances'],
line='g-',
label='decrement-0.9999')
fig.set_xscale('log')
fig.save(filename=filename, output_folder=output_folder)
def _genetic_sa_test_type_1_(graph,
runs,
iterations,
chromosomes,
selection,
subf="a",
mr=0.5):
cities = len(graph.nodes())
filename = str(cities) + "_genetic_sa_test_1_" + selection
output_folder = base_folder + "1/" + subf
c = cond()
c[CONDITION_GENERATION] = iterations
gp1 = g_pars(chromosomes, MUTATION_SWAP, selection, mr)
gp2 = g_pars(chromosomes, MUTATION_SA, selection, mr)
gp3 = g_pars(chromosomes, MUTATION_SA_INSERT, selection, mr)
gp4 = g_pars(chromosomes, MUTATION_SA_REVERSE, selection, mr)
runner = TSPSRunner(graph)
runner.add_tsps(Genetic, gp1, c, runs, 'm-swap')
runner.add_tsps(Genetic, gp2, c, runs, 'm-sa')
runner.add_tsps(Genetic, gp3, c, runs, 'm-sa-i')
runner.add_tsps(Genetic, gp4, c, runs, 'm-sa-r')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("GA-SA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['m-swap']['all_distances'],
line='r-',
label='m-swap')
fig.add_averaged_x_plot_values(runner.results['m-sa']['all_distances'],
line='g-',
label='m-sa')
fig.add_averaged_x_plot_values(runner.results['m-sa-i']['all_distances'],
line='c-',
label='m-sa-i')
fig.add_averaged_x_plot_values(runner.results['m-sa-r']['all_distances'],
line='y-',
label='m-sa-r')
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot("GA-SA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['m-swap']['all_distances'],
line='r-',
label='m-swap')
fig.add_averaged_x_plot_values(runner.results['m-sa']['all_distances'],
line='g-',
label='m-sa')
fig.add_averaged_x_plot_values(runner.results['m-sa-i']['all_distances'],
line='c-',
label='m-sa-i')
fig.add_averaged_x_plot_values(runner.results['m-sa-r']['all_distances'],
line='y-',
label='m-sa-r')
fig.set_xscale('log')
fig.save(filename=filename, output_folder=output_folder)
def _genetic_test_type_1_(graph, runs, iterations, chromosomes,
subf="a", crmr=(0.9, 0.1)):
cities = len(graph.nodes())
filename = str(cities) + "_genetic_test_1_crmr[" + str(crmr) + "]_"
output_folder = base_folder + "1/" + subf
c = cond()
c[CONDITION_GENERATION] = iterations
gp1 = {
'chromosomes': chromosomes,
'cr': crmr[0],
'mr': crmr[1],
'selection_method': SELECTION_ROULETE,
'crossover_method': CROSSOVER_ORDINAL_1P,
'mutation_method': MUTATION_SWAP
}
gp2 = {
'chromosomes': chromosomes,
'cr': crmr[0],
'mr': crmr[1],
'selection_method': SELECTION_RANK,
'crossover_method': CROSSOVER_ORDINAL_1P,
'mutation_method': MUTATION_SWAP
}
gp3 = {
'chromosomes': chromosomes,
'cr': crmr[0],
'mr': crmr[1],
'selection_method': SELECTION_ROULETE,
'crossover_method': CROSSOVER_OX,
'mutation_method': MUTATION_SWAP
}
gp4 = {
'chromosomes': chromosomes,
'cr': crmr[0],
'mr': crmr[1],
'selection_method': SELECTION_RANK,
'crossover_method': CROSSOVER_OX,
'mutation_method': MUTATION_SWAP
}
runner = TSPSRunner(graph)
runner.add_tsps(Genetic, gp1, c, runs, 'roulete-ordinal')
runner.add_tsps(Genetic, gp2, c, runs, 'rank-ordinal')
runner.add_tsps(Genetic, gp3, c, runs, 'roulete-ox')
runner.add_tsps(Genetic, gp4, c, runs, 'rank-ox')
runner.run()
runner.save_results(filename=filename, output_folder=output_folder)
fig = GenericFig(ylabel="Vidutinis atstumas", size=120)
fig.next_subplot("GA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['roulete-ordinal']
['all_distances'], line='r-',
label='roulete-ordinal')
fig.add_averaged_x_plot_values(runner.results['rank-ordinal']
['all_distances'], line='g-',
label='rank-ordinal')
fig.add_averaged_x_plot_values(runner.results['roulete-ox']
['all_distances'], line='c-',
label='roulete-ox')
fig.add_averaged_x_plot_values(runner.results['rank-ox']
['all_distances'], line='y-',
label='rank-ox')
fig.set_legend()
fig.set_ylabel('')
fig.next_subplot("GA, " + str(cities) + " miestu, n = " + str(runs))
fig.add_averaged_x_plot_values(runner.results['roulete-ordinal']
['all_distances'], line='r-',
label='roulete-ordinal')
fig.add_averaged_x_plot_values(runner.results['rank-ordinal']
['all_distances'], line='g-',
label='rank-ordinal')
fig.add_averaged_x_plot_values(runner.results['roulete-ox']
['all_distances'], line='c-',
label='roulete-ox')
fig.add_averaged_x_plot_values(runner.results['rank-ox']
['all_distances'], line='y-',
label='rank-ox')
fig.set_xscale('log')
fig.save(filename=filename, output_folder=output_folder)