80 lines
2.4 KiB
Python
80 lines
2.4 KiB
Python
import matplotlib.pyplot as plt
|
|
import random, time
|
|
from libs.clustering import split_tour_across_clusters
|
|
from libs.simulated_annealing import SimulatedAnnealing, total_distance
|
|
|
|
def generate_cities(nb, max_coords=1000):
|
|
return [random.sample(range(max_coords), 2) for _ in range(nb)]
|
|
|
|
nb_ville = 100
|
|
max_coords = 1000
|
|
nb_truck = 4
|
|
temperature = 10000
|
|
cooling_rate = 0.999
|
|
temperature_ok = 0.001
|
|
|
|
start_time_generate = time.time()
|
|
cities = generate_cities(nb_ville, max_coords)
|
|
cities[0] = [max_coords/2, max_coords/2]
|
|
stop_time_generate = time.time()
|
|
|
|
start_time_split = time.time()
|
|
clusters = split_tour_across_clusters(cities, nb_truck)
|
|
stop_time_split = time.time()
|
|
|
|
for cluster in clusters.values():
|
|
print(len(cluster))
|
|
print("\n---- TIME ----")
|
|
print("generate cities time: ", stop_time_generate - start_time_generate)
|
|
print("split cities time: ", stop_time_split - start_time_split)
|
|
|
|
# create new figure for annealing paths
|
|
plt.figure()
|
|
colors = [
|
|
'#1f77b4', # Bleu moyen
|
|
'#ff7f0e', # Orange
|
|
'#2ca02c', # Vert
|
|
'#d62728', # Rouge
|
|
'#9467bd', # Violet
|
|
'#8c564b', # Marron
|
|
'#e377c2', # Rose
|
|
'#7f7f7f', # Gris
|
|
'#bcbd22', # Vert olive
|
|
'#17becf', # Turquoise
|
|
'#1b9e77', # Vert Teal
|
|
'#d95f02', # Orange foncé
|
|
'#7570b3', # Violet moyen
|
|
'#e7298a', # Fuchsia
|
|
'#66a61e', # Vert pomme
|
|
'#e6ab02', # Jaune or
|
|
'#a6761d', # Bronze
|
|
'#666666', # Gris foncé
|
|
'#f781bf', # Rose clair
|
|
'#999999', # Gris moyen
|
|
]
|
|
|
|
best_routes = []
|
|
|
|
for i, cluster_indices in enumerate(clusters.values()):
|
|
# Sélection d'une couleur pour le cluster
|
|
color = colors[i % len(colors)]
|
|
|
|
# Récupération des coordonnées de la ville
|
|
cluster_cities = [cities[index] for index in cluster_indices]
|
|
|
|
# Appel de la fonction simulated_annealing
|
|
simulated_annealing = SimulatedAnnealing(cluster_cities, temperature=10000, cooling_rate=0.999, temperature_ok=0.01)
|
|
best_route = simulated_annealing.run()
|
|
best_routes.append((best_route, color))
|
|
|
|
print("Final solution for cluster ", i, ":", best_route)
|
|
print("Total distance: ", total_distance(best_route))
|
|
|
|
for i, (route, color) in enumerate(best_routes):
|
|
x = [city[0] for city in route]
|
|
y = [city[1] for city in route]
|
|
x.append(x[0])
|
|
y.append(y[0])
|
|
plt.plot(x, y, color=color, marker='x', linestyle='-', label=f"Cluster {i}")
|
|
plt.legend(loc="best")
|
|
plt.show() |