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Louis 2023-06-15 15:51:51 +02:00
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64
tests/01_cluster_splitter.py Normal file
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from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import random, time
from clustering import split_tour_across_clusters
def generate_cities(nb, max_coords=1000):
return [random.sample(range(max_coords), 2) for _ in range(nb)]
def plot_clusters(cities, clusters):
# Création d'une liste de couleurs pour les différents clusters
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
# Création d'un nouveau graphique
plt.figure()
# Pour chaque cluster
for i, cluster in clusters.items():
# Sélection d'une couleur pour le cluster
color = colors[i % len(colors)]
# Pour chaque ville dans le cluster
for city_index in cluster:
# Récupération des coordonnées de la ville
city = cities[city_index]
# Ajout de la ville au graphique
plt.scatter(city[0], city[1], c=color, s=20)
# show first city in black and twice bigger
plt.scatter(cities[0][0], cities[0][1], c='k', s=200)
# Affichage du graphique
plt.show()
nb_ville = 100
max_coords = 1000
nb_truck = 4
# Define the coordinates of the cities
# And set depot at the first city in the middle of the map
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()
# Split the tour across clusters with nb_truck trucks
start_time_split = time.time()
clusters = split_tour_across_clusters(cities, nb_truck)
stop_time_split = time.time()
# show the number of cities in each cluster
for cluster in clusters.values():
print(len(cluster))
# show the time
print("\n---- TIME ----")
print("generate cities time: ", stop_time_generate - start_time_generate)
print("split cities time: ", stop_time_split - start_time_split)
# show the clusters
plot_clusters(cities, clusters)

108
tests/02_cluster_recuit_live_animation.py Normal file
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from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import random, time, math
from clustering import split_tour_across_clusters
random.seed(1)
def generate_cities(nb, max_coords=1000):
return [random.sample(range(max_coords), 2) for _ in range(nb)]
def distance(city1, city2):
return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)
def total_distance(cities):
return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
previous_route = None
def draw_cities(cities, previous_route, color='b', title=' '):
plt.title(title)
# If there's a previous route, we remove it.
if previous_route is not None:
previous_route.remove()
x = [city[0] for city in cities]
y = [city[1] for city in cities]
x.append(x[0])
y.append(y[0])
# We plot the route with the specified color and keep a reference to the Line2D object.
previous_route, = plt.plot(x, y, color=color, marker='x', linestyle='-')
plt.draw()
plt.pause(0.005)
# We return the reference so we can remove this route when a new one is found.
return previous_route
def simulated_annealing(cities, color='b', temperature=100000, cooling_rate=0.9999, temperature_ok=0.001):
interration = 0
plt.ion()
current_solution = cities.copy()
best_solution = cities.copy()
previous_route = draw_cities(best_solution, None, color, 'Initial solution')
while temperature > temperature_ok:
new_solution = current_solution.copy()
# Swap two cities in the route
i = random.randint(0, len(new_solution) - 1)
j = random.randint(0, len(new_solution) - 1)
new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
# Calculate the acceptance probability
current_energy = total_distance(current_solution)
new_energy = total_distance(new_solution)
delta = new_energy - current_energy
if delta < 0 or random.random() < math.exp(-delta / temperature):
current_solution = new_solution
if total_distance(current_solution) < total_distance(best_solution):
best_solution = current_solution
previous_route = draw_cities(best_solution, previous_route, color, 'Current best solution, total distance = ' + str(total_distance(best_solution)) + ', iteration = ' + str(interration))
# Cool down
temperature *= cooling_rate
interration += 1
plt.ioff()
return best_solution
nb_ville = 100
max_coords = 1000
nb_truck = 4
temperature = 10000
cooling_rate = 0.999
temperature_ok = 0.000001
start_time_generate = time.time()
cities = generate_cities(nb_ville, max_coords) # generate 100 cities
cities[0] = [max_coords/2, max_coords/2] # placing depot at the center
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 = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
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 avec la couleur choisie
best_route = simulated_annealing(cluster_cities, color, temperature, cooling_rate, temperature_ok)
print("Final solution for cluster ", i, ":", best_route)
print("Total distance: ", total_distance(best_route))
plt.show()

115
tests/03_cluster_recuit_no_animation.py Normal file
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from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import random, time, math
from clustering import split_tour_across_clusters
def generate_cities(nb, max_coords=1000):
return [random.sample(range(max_coords), 2) for _ in range(nb)]
def distance(city1, city2):
return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)
def total_distance(cities):
return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
previous_route = None
def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001, cluster_index=0):
interration = 0
current_solution = cities.copy()
best_solution = cities.copy()
while temperature > temperature_ok:
new_solution = current_solution.copy()
# Swap two cities in the route
i = random.randint(0, len(new_solution) - 1)
j = random.randint(0, len(new_solution) - 1)
new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
# Calculate the acceptance probability
current_energy = total_distance(current_solution)
new_energy = total_distance(new_solution)
delta = new_energy - current_energy
if delta < 0 or random.random() < math.exp(-delta / temperature):
current_solution = new_solution
if total_distance(current_solution) < total_distance(best_solution):
best_solution = current_solution
# Cool down
temperature *= cooling_rate
interration += 1
# Print every 1000 iterations
if interration % 1000 == 0:
print("Cluster", cluster_index, ": iteration", interration, "with current total distance", total_distance(current_solution))
return best_solution
nb_ville = 20
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
best_route = simulated_annealing(cluster_cities, temperature, cooling_rate, temperature_ok)
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()

133
tests/04_cluster_ant_colony_no_animation.py Normal file
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from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import random, time, math
from clustering import split_tour_across_clusters
random.seed(2)
def generate_cities(nb, max_coords=1000):
return [random.sample(range(max_coords), 2) for _ in range(nb)]
def distance(city1, city2):
return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2) + 1e-10
def total_distance(cities):
return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
class AntColony:
def __init__(self, cities, n_ants, alpha=1, beta=2, evaporation=0.5, intensification=2, max_time=0.1):
self.cities = cities
self.n = len(cities)
self.n_ants = n_ants
self.alpha = alpha
self.beta = beta
self.evaporation = evaporation
self.intensification = intensification
self.max_time = max_time
self.pheromones = [[1 / self.n for _ in range(self.n)] for __ in range(self.n)]
def choose_next_city(self, ant):
unvisited_cities = [i for i in range(self.n) if i not in ant]
probabilities = [self.pheromones[ant[-1]][i] ** self.alpha * ((1 / distance(self.cities[ant[-1]], self.cities[i])) ** self.beta) for i in unvisited_cities]
total = sum(probabilities)
if total == 0:
probabilities = [1 / len(unvisited_cities) for _ in unvisited_cities]
else:
probabilities = [p / total for p in probabilities]
return np.random.choice(unvisited_cities, p=probabilities)
def update_pheromones(self, ant):
pheromones_delta = self.intensification / total_distance([self.cities[i] for i in ant])
for i in range(len(ant) - 1):
self.pheromones[ant[i]][ant[i+1]] += pheromones_delta
def run(self):
best_ant = []
best_distance = float('inf')
start_time = time.time()
while time.time() - start_time < self.max_time:
ants = [[random.randint(0, self.n - 1)] for _ in range(self.n_ants)]
for ant in ants:
for _ in range(self.n - 1):
ant.append(self.choose_next_city(ant))
ant_distance = total_distance([self.cities[i] for i in ant])
if ant_distance < best_distance:
best_distance = ant_distance
best_ant = ant.copy()
self.update_pheromones(ant)
self.pheromones = [[(1 - self.evaporation) * p for p in row] for row in self.pheromones]
return [self.cities[i] for i in best_ant]
nb_ville = 200
max_coords = 1000
nb_truck = 4
max_time = 5
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 AntColony.run
ant_colony = AntColony(cluster_cities, n_ants=10, max_time=max_time)
best_route = ant_colony.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}")
# add title with nb_ville, nb_truck and max_time
plt.title(f"nb_ville = {nb_ville}, nb_truck = {nb_truck}, max_time = {max_time}")
plt.show()

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tests/clustering.py Normal file
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from sklearn.cluster import KMeans
import numpy as np
def split_tour_across_clusters(cities, nb_truck):
if nb_truck == 1:
return {0: list(range(len(cities)))}
# clustering initial
kmeans = KMeans(n_clusters=nb_truck, random_state=0).fit(cities)
clusters = {i:[] for i in range(nb_truck)}
# assignation des indices des villes aux clusters
for i, label in enumerate(kmeans.labels_):
clusters[label].append(i)
max_iterations = len(cities)**2
iteration = 0
while True:
iteration += 1
if iteration > max_iterations:
print("Le nombre maximum d'itérations a été atteint. La boucle a été interrompue.")
break
# calcul des tailles de clusters
cluster_sizes = {i:len(clusters[i]) for i in range(nb_truck)}
# identification du cluster le plus grand et du plus petit
max_cluster = max(cluster_sizes, key=cluster_sizes.get)
min_cluster = min(cluster_sizes, key=cluster_sizes.get)
# s'il n'y a pas de grande disparité, on arrête la boucle
if cluster_sizes[max_cluster] - cluster_sizes[min_cluster] <= 1:
break
# calcul du centre de chaque cluster
cluster_centers = {i:np.mean([cities[index] for index in clusters[i]], axis=0) for i in range(nb_truck)}
# calcul des distances entre le centre du cluster le plus grand et les autres
distances = {i:np.linalg.norm(cluster_centers[max_cluster]-cluster_centers[i]) for i in range(nb_truck)}
del distances[max_cluster] # on supprime la distance vers lui-même
if nb_truck >= 3:
# on identifie les 2 clusters les plus proches
closest_clusters = sorted(distances, key=distances.get)[:2]
# parmi les deux clusters les plus proches, on choisit le plus petit
if cluster_sizes[closest_clusters[0]] <= cluster_sizes[closest_clusters[1]]:
target_cluster = closest_clusters[0]
else:
target_cluster = closest_clusters[1]
else:
closest_clusters = sorted(distances, key=distances.get)[:1]
target_cluster = closest_clusters[0]
# si le transfert va créer une plus grande disparité, on arrête la boucle
if cluster_sizes[target_cluster] >= cluster_sizes[max_cluster]:
break
# calcul des distances entre le centre du cluster cible et les villes du cluster le plus grand
distances_to_target = {index:np.linalg.norm(cluster_centers[target_cluster]-cities[index])
for index in clusters[max_cluster]}
# on identifie la ville la plus proche du centre du cluster cible
closest_city_index = min(distances_to_target, key=distances_to_target.get)
# on transfère la ville du cluster le plus grand au cluster cible
clusters[target_cluster].append(closest_city_index)
clusters[max_cluster].remove(closest_city_index)
# Ajout du point de départ et d'arrivée pour chaque cluster
depot_index = 0
for cluster in clusters.values():
if cluster[0] != depot_index:
cluster.insert(0, depot_index)
if cluster[-1] != depot_index:
cluster.append(depot_index)
return clusters

1
tests/data_sample/15_cities_minimum_293.txt Normal file
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[[-0.0, 0.0], [-21.5, -7.3], [-28.9, -0.0], [-43.1, -14.6], [-50.5, -7.4], [-64.7, -21.9], [-72.1, -0.2], [-79.3, 21.4], [-65.1, 36.1], [-57.6, 43.3], [-50.6, 21.6], [-36.0, 21.6], [-29.1, 43.2], [-14.7, 43.4], [-0.1, 28.7], [-0.0, 0.0]]

3
tests/data_sample/48_cities_minimum_33523.txt Normal file
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[[6734.0, 1453.0], [2233.0, 10.0], [5530.0, 1424.0], [401.0, 841.0], [3082.0, 1644.0], [7608.0, 4458.0], [7573.0, 3716.0], [7265.0, 1268.0], [6898.0, 1885.0], [1112.0, 2049.0], [5468.0, 2606.0], [5989.0, 2873.0], [4706.0, 2674.0], [4612.0, 2035.0], [6347.0, 2683.0], [6107.0, 669.0], [7611.0, 5184.0], [7462.0, 3590.0],
[7732.0, 4723.0], [5900.0, 3561.0], [4483.0, 3369.0], [6101.0, 1110.0], [5199.0, 2182.0], [1633.0, 2809.0], [4307.0, 2322.0], [675.0, 1006.0], [7555.0, 4819.0], [7541.0, 3981.0], [3177.0, 756.0], [7352.0, 4506.0], [7545.0, 2801.0], [3245.0, 3305.0], [6426.0, 3173.0], [4608.0, 1198.0], [23.0, 2216.0], [7248.0, 3779.0],
[7762.0, 4595.0], [7392.0, 2244.0], [3484.0, 2829.0], [6271.0, 2135.0], [4985.0, 140.0], [1916.0, 1569.0], [7280.0, 4899.0], [7509.0, 3239.0], [10.0, 2676.0], [6807.0, 2993.0], [5185.0, 3258.0], [3023.0, 1942.0]]