115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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import numpy as np
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import random, time, math
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from libs.clustering import split_tour_across_clusters
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def generate_cities(nb, max_coords=1000):
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return [random.sample(range(max_coords), 2) for _ in range(nb)]
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def distance(city1, city2):
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return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)
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def total_distance(cities):
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return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
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previous_route = None
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def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001, cluster_index=0):
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interration = 0
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current_solution = cities.copy()
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best_solution = cities.copy()
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while temperature > temperature_ok:
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new_solution = current_solution.copy()
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# Swap two cities in the route
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i = random.randint(0, len(new_solution) - 1)
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j = random.randint(0, len(new_solution) - 1)
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new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
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# Calculate the acceptance probability
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current_energy = total_distance(current_solution)
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new_energy = total_distance(new_solution)
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delta = new_energy - current_energy
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if delta < 0 or random.random() < math.exp(-delta / temperature):
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current_solution = new_solution
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if total_distance(current_solution) < total_distance(best_solution):
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best_solution = current_solution
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# Cool down
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temperature *= cooling_rate
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interration += 1
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# Print every 1000 iterations
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if interration % 1000 == 0:
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print("Cluster", cluster_index, ": iteration", interration, "with current total distance", total_distance(current_solution))
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return best_solution
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nb_ville = 20
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max_coords = 1000
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nb_truck = 4
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temperature = 10000
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cooling_rate = 0.999
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temperature_ok = 0.001
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start_time_generate = time.time()
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cities = generate_cities(nb_ville, max_coords)
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cities[0] = [max_coords/2, max_coords/2]
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stop_time_generate = time.time()
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start_time_split = time.time()
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clusters = split_tour_across_clusters(cities, nb_truck)
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stop_time_split = time.time()
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for cluster in clusters.values():
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print(len(cluster))
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print("\n---- TIME ----")
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print("generate cities time: ", stop_time_generate - start_time_generate)
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print("split cities time: ", stop_time_split - start_time_split)
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# create new figure for annealing paths
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plt.figure()
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colors = [
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'#1f77b4', # Bleu moyen
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'#ff7f0e', # Orange
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'#2ca02c', # Vert
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'#d62728', # Rouge
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'#9467bd', # Violet
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'#8c564b', # Marron
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'#e377c2', # Rose
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'#7f7f7f', # Gris
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'#bcbd22', # Vert olive
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'#17becf', # Turquoise
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'#1b9e77', # Vert Teal
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'#d95f02', # Orange foncé
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'#7570b3', # Violet moyen
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'#e7298a', # Fuchsia
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'#66a61e', # Vert pomme
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'#e6ab02', # Jaune or
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'#a6761d', # Bronze
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'#666666', # Gris foncé
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'#f781bf', # Rose clair
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'#999999', # Gris moyen
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]
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best_routes = []
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for i, cluster_indices in enumerate(clusters.values()):
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# Sélection d'une couleur pour le cluster
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color = colors[i % len(colors)]
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# Récupération des coordonnées de la ville
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cluster_cities = [cities[index] for index in cluster_indices]
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# Appel de la fonction simulated_annealing
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best_route = simulated_annealing(cluster_cities, temperature, cooling_rate, temperature_ok)
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best_routes.append((best_route, color))
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print("Final solution for cluster ", i, ":", best_route)
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print("Total distance: ", total_distance(best_route))
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for i, (route, color) in enumerate(best_routes):
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x = [city[0] for city in route]
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y = [city[1] for city in route]
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x.append(x[0])
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y.append(y[0])
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plt.plot(x, y, color=color, marker='x', linestyle='-', label=f"Cluster {i}")
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plt.legend(loc="best")
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plt.show() |