diff --git a/tests/01_cluster_splitter.py b/tests/01_cluster_splitter.py index c686b5b..ce3582a 100644 --- a/tests/01_cluster_splitter.py +++ b/tests/01_cluster_splitter.py @@ -2,7 +2,7 @@ from sklearn.cluster import KMeans import matplotlib.pyplot as plt import numpy as np import random, time -from clustering import split_tour_across_clusters +from libs.clustering import split_tour_across_clusters def generate_cities(nb, max_coords=1000): return [random.sample(range(max_coords), 2) for _ in range(nb)] diff --git a/tests/02_cluster_recuit_live_animation.py b/tests/02_cluster_recuit_live_animation.py index ca46ddf..d9a2a73 100644 --- a/tests/02_cluster_recuit_live_animation.py +++ b/tests/02_cluster_recuit_live_animation.py @@ -2,7 +2,7 @@ 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 +from libs.clustering import split_tour_across_clusters random.seed(1) @@ -66,7 +66,7 @@ def simulated_annealing(cities, color='b', temperature=100000, cooling_rate=0.99 plt.ioff() return best_solution -nb_ville = 100 +nb_ville = 200 max_coords = 1000 nb_truck = 4 temperature = 10000 diff --git a/tests/03_cluster_recuit_no_animation.py b/tests/03_cluster_recuit_no_animation.py index 3c07ce2..d678031 100644 --- a/tests/03_cluster_recuit_no_animation.py +++ b/tests/03_cluster_recuit_no_animation.py @@ -2,7 +2,7 @@ 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 +from libs.clustering import split_tour_across_clusters def generate_cities(nb, max_coords=1000): return [random.sample(range(max_coords), 2) for _ in range(nb)] diff --git a/tests/04_cluster_ant_colony_no_animation.py b/tests/04_cluster_ant_colony_no_animation.py index f75a771..fb2a4bd 100644 --- a/tests/04_cluster_ant_colony_no_animation.py +++ b/tests/04_cluster_ant_colony_no_animation.py @@ -2,9 +2,9 @@ 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 +from libs.clustering import split_tour_across_clusters -random.seed(2) +random.seed(3) def generate_cities(nb, max_coords=1000): return [random.sample(range(max_coords), 2) for _ in range(nb)] @@ -60,9 +60,9 @@ class AntColony: return [self.cities[i] for i in best_ant] nb_ville = 50 -max_coords = 10 -nb_truck = 1 -max_time = 1 +max_coords = 1000 +nb_truck = 2 +max_time = 3 nb_ants = 10 max_time_per_cluster = max_time / nb_truck @@ -123,6 +123,13 @@ for i, cluster_indices in enumerate(clusters.values()): print("Total distance for cluster", i, ": ", total_distance(best_route)) +# calculate total distance for all clusters +full_total_distance = 0 +for route, color in best_routes: + full_total_distance += total_distance(route) + +print("Total distance for all clusters: ", full_total_distance) + for i, (route, color) in enumerate(best_routes): x = [city[0] for city in route] y = [city[1] for city in route] diff --git a/tests/clustering.py b/tests/clustering.py index 02ff6df..e69de29 100644 --- a/tests/clustering.py +++ b/tests/clustering.py @@ -1,81 +0,0 @@ -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) - 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 \ No newline at end of file diff --git a/tests/data_sample/48_cities_minimum_33523.txt b/tests/data_sample/48_cities_minimum_33523.txt index 7ae51b2..6c92641 100644 --- a/tests/data_sample/48_cities_minimum_33523.txt +++ b/tests/data_sample/48_cities_minimum_33523.txt @@ -1,3 +1 @@ -[[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]] \ No newline at end of file +[['6734', '1453'], ['2233', '10'], ['5530', '1424'], ['401', '841'], ['3082', '1644'], ['7608', '4458'], ['7573', '3716'], ['7265', '1268'], ['6898', '1885'], ['1112', '2049'], ['5468', '2606'], ['5989', '2873'], ['4706', '2674'], ['4612', '2035'], ['6347', '2683'], ['6107', '669'], ['7611', '5184'], ['7462', '3590'], ['7732', '4723'], ['5900', '3561'], ['4483', '3369'], ['6101', '1110'], ['5199', '2182'], ['1633', '2809'], ['4307', '2322'], ['675', '1006'], ['7555', '4819'], ['7541', '3981'], ['3177', '756'], ['7352', '4506'], ['7545', '2801'], ['3245', '3305'], ['6426', '3173'], ['4608', '1198'], ['23', '2216'], ['7248', '3779'], ['7762', '4595'], ['7392', '2244'], ['3484', '2829'], ['6271', '2135'], ['4985', '140'], ['1916', '1569'], ['7280', '4899'], ['7509', '3239'], ['10', '2676'], ['6807', '2993'], ['5185', '3258'], ['3023', '1942']] \ No newline at end of file diff --git a/tests/libs/aco.py b/tests/libs/aco.py new file mode 100644 index 0000000..6fb6f8c --- /dev/null +++ b/tests/libs/aco.py @@ -0,0 +1,51 @@ +import math, random, time, numpy as np + +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] \ No newline at end of file diff --git a/tests/libs/clustering.py b/tests/libs/clustering.py new file mode 100644 index 0000000..02ff6df --- /dev/null +++ b/tests/libs/clustering.py @@ -0,0 +1,81 @@ +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) + 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 \ No newline at end of file diff --git a/tests/libs/simulated_annealing.py b/tests/libs/simulated_annealing.py new file mode 100644 index 0000000..827d442 --- /dev/null +++ b/tests/libs/simulated_annealing.py @@ -0,0 +1,33 @@ +import math, random + +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))]) + +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 \ No newline at end of file