142 lines
4.9 KiB
Python
142 lines
4.9 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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random.seed(3)
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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) + 1e-10
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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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class AntColony:
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def __init__(self, cities, n_ants, alpha=1, beta=2, evaporation=0.5, intensification=2, max_time=0.1):
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self.cities = cities
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self.n = len(cities)
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self.n_ants = n_ants
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self.alpha = alpha
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self.beta = beta
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self.evaporation = evaporation
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self.intensification = intensification
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self.max_time = max_time
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self.pheromones = [[1 / self.n for _ in range(self.n)] for __ in range(self.n)]
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def choose_next_city(self, ant):
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unvisited_cities = [i for i in range(self.n) if i not in ant]
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probabilities = [self.pheromones[ant[-1]][i] ** self.alpha * ((1 / distance(self.cities[ant[-1]], self.cities[i])) ** self.beta) for i in unvisited_cities]
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total = sum(probabilities)
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if total == 0:
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probabilities = [1 / len(unvisited_cities) for _ in unvisited_cities]
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else:
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probabilities = [p / total for p in probabilities]
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return np.random.choice(unvisited_cities, p=probabilities)
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def update_pheromones(self, ant):
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pheromones_delta = self.intensification / total_distance([self.cities[i] for i in ant])
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for i in range(len(ant) - 1):
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self.pheromones[ant[i]][ant[i+1]] += pheromones_delta
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def run(self):
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best_ant = []
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best_distance = float('inf')
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start_time = time.time()
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while time.time() - start_time < self.max_time:
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ants = [[random.randint(0, self.n - 1)] for _ in range(self.n_ants)]
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for ant in ants:
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for _ in range(self.n - 1):
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ant.append(self.choose_next_city(ant))
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ant_distance = total_distance([self.cities[i] for i in ant])
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if ant_distance < best_distance:
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best_distance = ant_distance
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best_ant = ant.copy()
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self.update_pheromones(ant)
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self.pheromones = [[(1 - self.evaporation) * p for p in row] for row in self.pheromones]
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return [self.cities[i] for i in best_ant]
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nb_ville = 2000
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max_coords = 1000
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nb_truck = 3
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max_time = 50
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nb_ants = 10
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max_time_per_cluster = max_time / nb_truck
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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 AntColony.run
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ant_colony = AntColony(cluster_cities, n_ants=nb_ants, max_time=max_time_per_cluster)
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best_route = ant_colony.run()
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best_routes.append((best_route, color))
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print("Total distance for cluster", i, ": ", total_distance(best_route))
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# calculate total distance for all clusters
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full_total_distance = 0
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for route, color in best_routes:
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full_total_distance += total_distance(route)
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print("Total distance for all clusters: ", full_total_distance)
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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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# add title with nb_ville, nb_truck and max_time
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plt.title(f"nb_ville = {nb_ville}, nb_truck = {nb_truck}, max_time = {max_time}")
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plt.show() |