def kakak():
ga_tsp = GA_TSP(func=cal_total_weight,
n_dim=flow_nums,
size_pop=50,
max_iter=200,
prob_mut=0.06)
best_goods, best_value = ga_tsp.run()
print(best_goods, best_value)
point2(ga_tsp)
def _GA_start(flows_len):
if(flows_len%2!=0):
flows_len=flows_len-1
len=flows_len
ga_tsp = GA_TSP(func=_GA_Fit, n_dim=len, size_pop=len, max_iter=200,prob_mut=0.06)
best_goods, best_value = ga_tsp.run()
print(best_goods, best_value)
#point2(ga_tsp)
return best_goods
def kaka(self):
ga_tsp = GA_TSP(func=cal_total_weight,
n_dim=flow_nums,
size_pop=flow_nums,
max_iter=500,
prob_mut=0.01)
best_goods, best_value = ga_tsp.run()
print(best_goods, best_value)
fit_history = pd.DataFrame(ga_tsp.all_history_Y)
fig, ax = plt.subplots(2, 1)
ax[0].plot(fit_history.index, fit_history.values, '.', color='red')
plt_min = fit_history.min(axis=1)
ax[1].plot(plt_min.index, plt_min, Label='min')
ax[1].plot(plt_min.index, plt_min.cummin())
# fit_history.max(axis=1).cummin().plot(kind='line')
plt.show()
def Get_Data(_num_points=20):
num_points = _num_points
points_coordinate = np.random.rand(num_points,
2) * 10 # generate coordinate of points
distance_matrix = spatial.distance.cdist(points_coordinate,
points_coordinate,
metric='euclidean')
def cal_total_distance(routine):
'''The objective function. input routine, return total distance.
cal_total_distance(np.arange(num_points))
'''
num_points, = routine.shape
return sum([
distance_matrix[routine[i % num_points],
routine[(i + 1) % num_points]]
for i in range(num_points)
])
# %% do GA
ga_tsp = GA_TSP(func=cal_total_distance,
n_dim=num_points,
size_pop=50,
max_iter=500,
prob_mut=1)
best_points, best_distance = ga_tsp.run()
return points_coordinate.tolist(), best_points, best_distance
def getTotalDistance(routine):
num_points, = routine.shape
return sum([
distanceMatrix[routine[i % num_points], routine[(i + 1) % num_points]]
for i in range(num_points)
])
ga_tsp = GA_TSP(func=getTotalDistance,
n_dim=NUM_POINT,
size_pop=50,
max_iter=500,
prob_mut=1)
best_points, best_distance = ga_tsp.run()
print('\n遗传旅行商\n')
print(best_points, '\nbest Distance:', best_distance)
# fig, ax = plt.subplots(1, 2)
# best_points_ = np.concatenate([best_points, [best_points[0]]])
# best_points_coordinate = pointsCoordinate[best_points_, :]
# ax[0].plot(best_points_coordinate[:, 0], best_points_coordinate[:, 1], 'o-r')
# ax[1].plot(ga_tsp.generation_best_Y)
# plt.show()
'''
粒子群算法
'''
def func3(x):
def runGA():
ga_tsp = GA_TSP(func=cal_total_distance, n_dim=num_points, size_pop=8, max_iter=10, prob_mut=0.1)
return ga_tsp.run()
