import numpy as np
# step1: randomly generate the data
from sko.demo_func import function_for_TSP
num_points, points_coordinate, distance_matrix, cal_total_distance = function_for_TSP(
num_points=15)
# %%step2: DO GA with UDF
from sko.GA import GA_TSP
from sko.operators import ranking, selection, crossover, mutation
ga_tsp = GA_TSP(func=cal_total_distance,
n_dim=num_points,
size_pop=50,
max_iter=100,
prob_mut=1)
ga_tsp.register('selection', selection.selection_tournament, tourn_size=3). \
register('mutation', mutation.mutation_reverse)
best_points, best_distance = ga_tsp.run()
print('best routine:', best_points, 'best_distance:', best_distance)
# %%
# step3: plot
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax.plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
plt.show()
num_points, = routine.shape
return sum([
distance_matrix[routine[i % num_points], routine[(i + 1) % num_points]]
for i in range(num_points)
])
from sko.GA import GA_TSP
# 3 * size_pop * max_iter
ga_tsp = GA_TSP(func=cal_total_distance,
n_dim=num_points,
size_pop=100,
max_iter=2400,
prob_mut=0.07)
ga_tsp.register(operator_name='crossover', operator=operators.crossover_ox)
best_points, best_distance = ga_tsp.run()
X = data_cities[best_points, 1]
Y = data_cities[best_points, 2]
plt.figure(figsize=(12, 8))
# plt.title('TSP Graph')
plt.title('GA: ' + str(best_distance))
plt.xlabel('X')
plt.ylabel('Y')
plt.scatter(X, Y, s=140)
for ind in range(num_points):
plt.plot((X[ind], X[(ind + 1) % num_points]),
(Y[ind], Y[(ind + 1) % num_points]),
import numpy as np
# step1: randomly generate the data
from sko.demo_func import function_for_TSP
num_points, points_coordinate, distance_matrix, cal_total_distance = function_for_TSP(
num_points=15)
# step2: DO GA with UDF
from sko.GA import GA_TSP
from sko.GA import selection_tournament, mutation_TSP_1
ga_tsp = GA_TSP(func=cal_total_distance,
n_dim=num_points,
size_pop=500,
max_iter=800,
Pm=0.2)
ga_tsp.register('selection', selection_tournament, tourn_size=3). \
register('mutation', mutation_TSP_1)
best_points, best_distance = ga_tsp.fit()
print('best routine:', best_points, 'best_distance:', best_distance)
# %%
# step3: plot
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
best_points_ = np.concatenate([best_points, [best_points[0]]])
best_points_coordinate = points_coordinate[best_points_, :]
ax.plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
plt.show()
