Merge branch 'Test' into 'main'

Test

See merge request axok/a3-algorithmique-avancee!3

.gitignore

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+*.pyc

tests/01_cluster_splitter.py

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+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import numpy as np
+import random, time
+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)]
+
+def plot_clusters(cities, clusters):
+    # Création d'une liste de couleurs pour les différents clusters
+    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
+]
+
+    # 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 = 1000
+max_coords = 1000
+nb_truck = 2
+
+# 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)

tests/02_cluster_recuit_live_animation.py

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+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import numpy as np
+import random, time, math
+from libs.clustering import split_tour_across_clusters
+from libs.simulated_annealing import total_distance
+
+def generate_cities(nb, max_coords=1000):
+    return [random.sample(range(max_coords), 2) for _ in range(nb)]
+
+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 = 50
+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()

tests/03_cluster_recuit_no_animation.py

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+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()

tests/04_cluster_ant_colony_no_animation.py

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+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import numpy as np
+import random, time, math
+from libs.clustering import split_tour_across_clusters
+
+random.seed(3)
+
+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 = 50
+max_coords = 1000
+nb_truck = 2
+max_time = 3
+nb_ants = 10
+
+max_time_per_cluster = max_time / nb_truck
+
+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=nb_ants, max_time=max_time_per_cluster)
+    best_route = ant_colony.run()
+    best_routes.append((best_route, color))
+
+    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]
+    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()

tests/05_cluster_ant_colony_recuit_no_animation.py

@@ -0,0 +1,166 @@
+from sklearn.cluster import KMeans
+import matplotlib.pyplot as plt
+import numpy as np
+import random, time, math
+from libs.clustering import split_tour_across_clusters
+
+random.seed(3)
+
+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]
+
+def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.1):
+    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
+    return best_solution
+
+nb_ville = 50
+max_coords = 1000
+nb_truck = 2
+max_time = 3
+nb_ants = 10
+
+max_time_per_cluster = max_time / nb_truck
+
+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=nb_ants, max_time=max_time_per_cluster)
+    best_route = ant_colony.run()
+    best_route = simulated_annealing(best_route)
+    best_routes.append((best_route, color))
+
+    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]
+    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()

tests/101_analyse_aco.py

@@ -0,0 +1,81 @@
+from matplotlib import pyplot as plt
+from libs.aco  import AntColony, total_distance
+
+cities = [[37.4393516691, 541.2090699418], [612.1759508571, 494.3166877396], [38.1312338227, 353.1484581781], [53.4418081065, 131.484901365], [143.0606355347, 631.7200953923], [689.9451267256, 468.5354998742], [112.7478815786, 529.417757826], [141.4875865042, 504.818485571], [661.0513901702, 445.9375182115], [98.7899036592, 384.5926031158], [697.3881696597, 180.3962284275], [536.4894189738, 287.2279085051], [192.4067320507, 20.439405931], [282.7865258765, 229.8001556189], [240.8251726391, 281.51414372], [246.9281323057, 322.461332116], [649.7313216456, 62.3331575282], [352.96585626, 666.7873101942], [633.392367658, 534.9398453712], [488.311799404, 437.4869439948], [141.4039286509, 228.4325551488], [17.3632612602, 240.2407068508], [397.5586451389, 231.3591208928], [565.7853781464, 282.3858748974], [475.8975387047, 468.5392706317], [322.4224566559, 550.3165478233], [397.5586634023, 74.7588387765], [672.8618339396, 432.882640963], [571.2189680147, 530.261699153], [104.6531165914, 482.8224768783], [356.7098388794, 67.6477131712], [400.4070255527, 253.6794479997], [282.3036243109, 426.8380500923], [58.7766988363, 507.1712386832], [189.75062244, 460.3815233617], [659.9124120147, 226.6284156239], [639.0307636033, 467.2302300719], [415.0258357432, 233.3045376118], [547.2662016307, 161.6589278401], [616.6547902644, 339.3409309407], [494.8592427417, 148.1217856389], [629.9980812186, 433.4548164038], [471.101431241, 314.2219307579], [138.2440514421, 137.1679919735], [91.5847556724, 110.0203007516], [390.6972811808, 423.9774318385], [565.1617825137, 429.1598152874], [54.5248980387, 438.5515408431], [334.350832971, 153.796923804], [531.0291024509, 612.3874827889], [475.7345905802, 385.7844618897], [228.8325218994, 410.4461939615], [578.3805347586, 321.3303494537], [358.9170574485, 404.4670352898], [486.4648554867, 593.0429937016], [343.169370767, 509.3123571315], [530.3626972076, 137.6881275684], [498.8065475299, 576.2102674608], [224.31827155, 312.4677490415], [595.836073259, 81.8130051356], [661.5588724308, 217.0456944477], [43.6892045516, 305.4722789165], [79.465345253, 445.9641737689], [210.4163247004, 130.7151137038], [432.2642292251, 629.4092661116], [623.2487161301, 69.189285084], [436.5194739944, 282.935645607], [59.4163265482, 40.1280234442], [630.9230074073, 230.342988813], [579.3265539688, 601.0359410602], [117.862450748, 112.9796833705], [297.7912565664, 166.3131886803], [22.7642703744, 455.5340094037], [259.7095810385, 10.6199925885], [342.3579873647, 599.3880182608], [10.0260950143, 
+488.9310558282], [315.2926064118, 273.2275475579], [220.7044919297, 270.0819745721], [192.1186059948, 314.1839922798], [271.5042718992, 225.2921989972], [530.7320005441, 504.0670155337], [42.5331441666, 656.3645162886], [396.1274792588, 539.4648066027], [118.6631474021, 508.7129103929], [395.6913876595, 699.5376048429], [559.0157105844, 560.8866941411], [22.6471035906, 526.2470392816], [135.6377085256, 325.8409901555], [141.4507014379, 485.2477927763], [396.7741299332, 460.7557115283], [87.7494562765, 19.6170129082], [350.4245639661, 420.6531186835], [216.7010817133, 466.4816410995], [130.9237737024, 351.1491733079], [72.6329856671, 645.7852219213], [144.6002949996, 457.4224283926], [212.3725077442, 594.9216893413], [49.9186786455, 541.4350825349], [656.6943525585, 558.1109593509], [176.5941623792, 648.5239953299], [500.3825200226, 198.7428378322], [634.317867842, 612.8291643194], [59.7537372726, 551.6321886765], [15.2145765106, 143.0441928532], [283.0054378872, 376.4439530184], [146.5389000907, 39.4231794338], [101.8685605377, 635.098685018], [588.1968537448, 580.5946976921], [457.2628632528, 350.0164047376], [537.4663680494, 472.5842276692], [269.3669098585, 367.4763636538], [239.9045383695, 102.629765339], [88.4677500396, 384.0507209275], [658.9133693395, 583.9575181023], [97.7359146347, 157.4558657632], [506.6191384007, 233.0022156094], [500.2566898239, 64.9136393489], [594.4048565021, 275.874186899], [66.230814661, 24.1317387604], [598.4162993909, 414.5557574275], [172.308833083, 344.3963466366], [299.48128518, 251.829512132], [303.8379894831, 21.052606379], [197.896926984, 512.388896098], [56.0199567669, 243.0663818382], [255.5566183121, 448.8651882442], [608.4256112402, 222.5421309272], [70.2722703273, 77.9227026433], [398.2298999899, 119.557657386], [635.4970237093, 133.3225902609], [378.3484559418, 272.2907677147], [484.8029663388, 677.0730379436], [278.8710882619, 299.9308770828], [381.6537300653, 360.3337602785], [557.6070707573, 595.3185092281], [249.0589749342, 76.6595112599], [562.9048787838, 670.0382113114], [398.550436558, 392.6493259144], [590.893972056, 370.7414913742], [558.2008003726, 0.4198814512], [461.4114714423, 530.5254969413], [354.7242881504, 685.40453619], [193.6611295657, 
+669.7432521028], [352.3140807211, 140.3273323662], [308.434570974, 115.2054269847], [299.588137008, 530.588961902], [334.2748764383, 152.1494569394], [690.9658585947, 134.5793307203], [48.0798124069, 270.968067372], [91.6467647724, 166.3541158474]]
+optimal = 6528
+
+n_ants = 10
+alpha = 1
+beta = 2
+evaporation = 0.5
+intensification = 2 
+max_times = [1, 2, 5]
+iterations = 2
+
+best_distances = []
+times = []
+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
+]
+
+for max_time in max_times:
+    for iteration in range(iterations):
+        ant_colony = AntColony(cities, n_ants, alpha, beta, evaporation, intensification, max_time)
+        print("Running iteration number {}/{} ({} sec)".format(iteration + 1, iterations, max_time))
+        best_route = ant_colony.run()
+        best_distances.append([total_distance(best_route), colors[max_times.index(max_time) % len(colors)]])
+        times.append(max_time)
+
+title = ""
+title += "Best distance per iterations\n"
+title += "Ants: " + str(n_ants) + " "
+title += "Alpja: " + str(alpha) + " "
+title += "Beta: " + str(beta) + " "
+title += "Evaporation: " + str(evaporation) + " "
+title += "Intensification: " + str(intensification) + " "
+title += "Max time: " + str(max_time)
+plt.title(title)
+plt.xlabel('Iteration')
+plt.ylabel('Distance')
+plt.axhline(y=optimal, color='r')
+
+distances = [x[0] for x in best_distances]  # Extractions des valeurs
+
+max_dist = max(distances)
+plt.ylim(0, max_dist+max_dist*0.2)
+
+values = [item[0] for item in best_distances]
+colors = [item[1] for item in best_distances]
+
+bars = plt.bar(range(len(values)), values, color=colors)
+
+for i, bar in enumerate(bars):
+    yval = bar.get_height()
+    plt.text(bar.get_x() + bar.get_width()/2, yval + 0.05, 
+             "dist: {}\ntime: {}s".format(int(yval), times[i]), 
+             rotation=75, ha='center', va='bottom')
+
+plt.xticks(range(len(values)), [str(i+1) for i in range(len(values))])
+
+plt.show()

tests/102_analyse_simulated_annealing.py

@@ -0,0 +1,75 @@
+from matplotlib import pyplot as plt
+from libs.simulated_annealing import SimulatedAnnealing, total_distance
+
+cities = [[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]]
+optimal = 33523
+
+temperatures = [100000, 1000000, 10000000, 100000000]
+cooling_rate = 0.99999
+temperature_ok = 0.000001
+iterations = 2
+
+best_distances = []
+temps = []
+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
+]
+
+for temperature in temperatures:
+    for iteration in range(iterations):
+        simulated_annealing = SimulatedAnnealing(cities, temperature=temperature, cooling_rate=0.999, temperature_ok=0.01)
+        print("Running iteration number {}/{} ({} temperature)".format(iteration + 1, iterations, temperature))
+        best_route = simulated_annealing.run()
+        best_distances.append([total_distance(best_route), colors[temperatures.index(temperature) % len(colors)]])
+        temps.append(temperature)
+
+title = ""
+title += "Best distance per iterations\n"
+title += "Temperature: " + str(temperature) + " "
+title += "Cooling rate: " + str(cooling_rate) + " "
+title += "Temperature ok: " + str(temperature_ok) + " "
+plt.title(title)
+plt.xlabel('Iteration')
+plt.ylabel('Distance')
+plt.axhline(y=optimal, color='r')
+
+distances = [x[0] for x in best_distances]  # Extractions des valeurs
+
+for best_distance in best_distances:
+    print(best_distance)
+max_dist = max(distances)
+plt.ylim(0, max_dist+max_dist*0.2)
+
+values = [item[0] for item in best_distances]
+colors = [item[1] for item in best_distances]
+
+bars = plt.bar(range(len(values)), values, color=colors)
+
+for i, bar in enumerate(bars):
+    yval = bar.get_height()
+    plt.text(bar.get_x() + bar.get_width()/2, yval + 0.05, 
+             "dist: {}\ntime: {}s".format(int(yval), temps[i]), 
+             rotation=75, ha='center', va='bottom')
+
+plt.xticks(range(len(values)), [str(i+1) for i in range(len(values))])
+
+plt.show()

tests/data_sample/150_cities_minimum_6528.txt

@@ -0,0 +1,3 @@
+[[37.4393516691, 541.2090699418], [612.1759508571, 494.3166877396], [38.1312338227, 353.1484581781], [53.4418081065, 131.484901365], [143.0606355347, 631.7200953923], [689.9451267256, 468.5354998742], [112.7478815786, 529.417757826], [141.4875865042, 504.818485571], [661.0513901702, 445.9375182115], [98.7899036592, 384.5926031158], [697.3881696597, 180.3962284275], [536.4894189738, 287.2279085051], [192.4067320507, 20.439405931], [282.7865258765, 229.8001556189], [240.8251726391, 281.51414372], [246.9281323057, 322.461332116], [649.7313216456, 62.3331575282], [352.96585626, 666.7873101942], [633.392367658, 534.9398453712], [488.311799404, 437.4869439948], [141.4039286509, 228.4325551488], [17.3632612602, 240.2407068508], [397.5586451389, 231.3591208928], [565.7853781464, 282.3858748974], [475.8975387047, 468.5392706317], [322.4224566559, 550.3165478233], [397.5586634023, 74.7588387765], [672.8618339396, 432.882640963], [571.2189680147, 530.261699153], [104.6531165914, 482.8224768783], [356.7098388794, 67.6477131712], [400.4070255527, 253.6794479997], [282.3036243109, 426.8380500923], [58.7766988363, 507.1712386832], [189.75062244, 460.3815233617], [659.9124120147, 226.6284156239], [639.0307636033, 467.2302300719], [415.0258357432, 233.3045376118], [547.2662016307, 161.6589278401], [616.6547902644, 339.3409309407], [494.8592427417, 148.1217856389], [629.9980812186, 433.4548164038], [471.101431241, 314.2219307579], [138.2440514421, 137.1679919735], [91.5847556724, 110.0203007516], [390.6972811808, 423.9774318385], [565.1617825137, 429.1598152874], [54.5248980387, 438.5515408431], [334.350832971, 153.796923804], [531.0291024509, 612.3874827889], [475.7345905802, 385.7844618897], [228.8325218994, 410.4461939615], [578.3805347586, 321.3303494537], [358.9170574485, 404.4670352898], [486.4648554867, 593.0429937016], [343.169370767, 509.3123571315], [530.3626972076, 137.6881275684], [498.8065475299, 576.2102674608], [224.31827155, 312.4677490415], [595.836073259, 81.8130051356], [661.5588724308, 217.0456944477], [43.6892045516, 305.4722789165], [79.465345253, 445.9641737689], [210.4163247004, 130.7151137038], [432.2642292251, 629.4092661116], [623.2487161301, 69.189285084], [436.5194739944, 282.935645607], [59.4163265482, 40.1280234442], [630.9230074073, 230.342988813], [579.3265539688, 601.0359410602], [117.862450748, 112.9796833705], [297.7912565664, 166.3131886803], [22.7642703744, 455.5340094037], [259.7095810385, 10.6199925885], [342.3579873647, 599.3880182608], [10.0260950143, 
+488.9310558282], [315.2926064118, 273.2275475579], [220.7044919297, 270.0819745721], [192.1186059948, 314.1839922798], [271.5042718992, 225.2921989972], [530.7320005441, 504.0670155337], [42.5331441666, 656.3645162886], [396.1274792588, 539.4648066027], [118.6631474021, 508.7129103929], [395.6913876595, 699.5376048429], [559.0157105844, 560.8866941411], [22.6471035906, 526.2470392816], [135.6377085256, 325.8409901555], [141.4507014379, 485.2477927763], [396.7741299332, 460.7557115283], [87.7494562765, 19.6170129082], [350.4245639661, 420.6531186835], [216.7010817133, 466.4816410995], [130.9237737024, 351.1491733079], [72.6329856671, 645.7852219213], [144.6002949996, 457.4224283926], [212.3725077442, 594.9216893413], [49.9186786455, 541.4350825349], [656.6943525585, 558.1109593509], [176.5941623792, 648.5239953299], [500.3825200226, 198.7428378322], [634.317867842, 612.8291643194], [59.7537372726, 551.6321886765], [15.2145765106, 143.0441928532], [283.0054378872, 376.4439530184], [146.5389000907, 39.4231794338], [101.8685605377, 635.098685018], [588.1968537448, 580.5946976921], [457.2628632528, 350.0164047376], [537.4663680494, 472.5842276692], [269.3669098585, 367.4763636538], [239.9045383695, 102.629765339], [88.4677500396, 384.0507209275], [658.9133693395, 583.9575181023], [97.7359146347, 157.4558657632], [506.6191384007, 233.0022156094], [500.2566898239, 64.9136393489], [594.4048565021, 275.874186899], [66.230814661, 24.1317387604], [598.4162993909, 414.5557574275], [172.308833083, 344.3963466366], [299.48128518, 251.829512132], [303.8379894831, 21.052606379], [197.896926984, 512.388896098], [56.0199567669, 243.0663818382], [255.5566183121, 448.8651882442], [608.4256112402, 222.5421309272], [70.2722703273, 77.9227026433], [398.2298999899, 119.557657386], [635.4970237093, 133.3225902609], [378.3484559418, 272.2907677147], [484.8029663388, 677.0730379436], [278.8710882619, 299.9308770828], [381.6537300653, 360.3337602785], [557.6070707573, 595.3185092281], [249.0589749342, 76.6595112599], [562.9048787838, 670.0382113114], [398.550436558, 392.6493259144], [590.893972056, 370.7414913742], [558.2008003726, 0.4198814512], [461.4114714423, 530.5254969413], [354.7242881504, 685.40453619], [193.6611295657, 
+669.7432521028], [352.3140807211, 140.3273323662], [308.434570974, 115.2054269847], [299.588137008, 530.588961902], [334.2748764383, 152.1494569394], [690.9658585947, 134.5793307203], [48.0798124069, 270.968067372], [91.6467647724, 166.3541158474]]

tests/data_sample/15_cities_minimum_284.txt

@@ -0,0 +1 @@
+[[-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]]

tests/data_sample/48_cities_minimum_33523.txt

@@ -0,0 +1 @@
+[['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']]

tests/data_sample/76_cities_minimum_538.txt

@@ -0,0 +1,3 @@
+[['22', '22'], ['36', '26'], ['21', '45'], ['45', '35'], ['55', '20'], ['33', '34'], ['50', '50'], ['55', '45'], ['26', '59'], ['40', '66'], ['55', '65'], ['35', '51'], ['62', '35'], ['62', '57'], ['62', '24'], ['21', '36'], ['33', '44'], ['9', '56'], ['62', '48'], ['66', '14'], 
+['44', '13'], ['26', '13'], ['11', '28'], ['7', '43'], ['17', '64'], ['41', '46'], ['55', '34'], ['35', '16'], ['52', '26'], ['43', '26'], ['31', '76'], ['22', '53'], ['26', '29'], ['50', '40'], ['55', '50'], ['54', '10'], ['60', '15'], ['47', '66'], ['30', '60'], ['30', '50'], ['12', '17'], ['15', '14'], ['16', '19'], ['21', '48'], ['50', '30'], ['51', '42'], ['50', '15'], ['48', '21'], ['12', '38'], ['15', '56'], ['29', '39'], ['54', '38'], ['55', '57'], ['67', '41'], ['10', '70'], ['6', '25'], ['65', '27'], ['40', '60'], ['70', '64'], ['64', '4'], ['36', '6'], ['30', '20'], ['20', '30'], ['15', '5'], ['50', '70'], ['57', '72'], ['45', '42'], ['38', '33'], ['50', '4'], ['66', '8'], ['59', 
+'5'], ['35', '60'], ['27', '24'], ['40', '20'], ['40', '37'], ['40', '40']]

tests/data_sample/ALL_tsp/a280.tsp

@@ -0,0 +1,287 @@
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+COMMENT : drilling problem (Ludwig)
+TYPE : TSP
+DIMENSION: 280
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+EOF

tests/data_sample/ALL_tsp/berlin52.opt.tour

@@ -0,0 +1,58 @@
+NAME : berlin52.opt.tour
+TYPE : TOUR
+DIMENSION : 52
+TOUR_SECTION
+1
+49
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+31
+22
+-1
+EOF

tests/data_sample/ALL_tsp/berlin52.tsp

@@ -0,0 +1,60 @@
+NAME: berlin52
+TYPE: TSP
+COMMENT: 52 locations in Berlin (Groetschel)
+DIMENSION: 52
+EDGE_WEIGHT_TYPE: EUC_2D
+NODE_COORD_SECTION
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+EOF
+

tests/data_sample/ALL_tsp/ch150.tsp

@@ -0,0 +1,157 @@
+NAME: ch150
+TYPE: TSP
+COMMENT: 150 city Problem (churritz)
+DIMENSION: 150
+EDGE_WEIGHT_TYPE: EUC_2D
+NODE_COORD_SECTION
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+EOF

tests/data_sample/ALL_tsp/eil76.tsp

@@ -0,0 +1,83 @@
+NAME : eil76
+COMMENT : 76-city problem (Christofides/Eilon)
+TYPE : TSP
+DIMENSION : 76
+EDGE_WEIGHT_TYPE : EUC_2D
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+EOF

tests/data_sample/ALL_tsp/fri26.tsp

@@ -0,0 +1,362 @@
+NAME: fri26
+TYPE: TSP
+COMMENT: 26 Staedte (Fricker)
+DIMENSION: 26
+EDGE_WEIGHT_TYPE: EXPLICIT
+EDGE_WEIGHT_FORMAT: LOWER_DIAG_ROW 
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+90
+0
+EOF
+
+
+

tests/data_sample/ALL_tsp/gr17.tsp

@@ -0,0 +1,21 @@
+NAME: gr17
+TYPE: TSP
+COMMENT: 17-city problem (Groetschel)
+DIMENSION: 17
+EDGE_WEIGHT_TYPE: EXPLICIT
+EDGE_WEIGHT_FORMAT: LOWER_DIAG_ROW 
+EDGE_WEIGHT_SECTION
+ 0 633 0 257 390 0 91 661 228 0 412 227
+ 169 383 0 150 488 112 120 267 0 80 572 196
+ 77 351 63 0 134 530 154 105 309 34 29 0
+ 259 555 372 175 338 264 232 249 0 505 289 262
+ 476 196 360 444 402 495 0 353 282 110 324 61
+ 208 292 250 352 154 0 324 638 437 240 421 329
+ 297 314 95 578 435 0 70 567 191 27 346 83
+ 47 68 189 439 287 254 0 211 466 74 182 243
+ 105 150 108 326 336 184 391 145 0 268 420 53
+ 239 199 123 207 165 383 240 140 448 202 57 0
+ 246 745 472 237 528 364 332 349 202 685 542 157
+ 289 426 483 0 121 518 142 84 297 35 29 36
+ 236 390 238 301 55 96 153 336 0 
+EOF

tests/data_sample/ALL_tsp/gr24.tsp

@@ -0,0 +1,33 @@
+NAME: gr24
+TYPE: TSP
+COMMENT: 24-city problem (Groetschel)
+DIMENSION: 24
+EDGE_WEIGHT_TYPE: EXPLICIT
+EDGE_WEIGHT_FORMAT: LOWER_DIAG_ROW 
+EDGE_WEIGHT_SECTION
+ 0 257 0 187 196 0 91 228 158 0 150 112
+ 96 120 0 80 196 88 77 63 0 130 167 59
+ 101 56 25 0 134 154 63 105 34 29 22 0
+ 243 209 286 159 190 216 229 225 0 185 86 124
+ 156 40 124 95 82 207 0 214 223 49 185 123
+ 115 86 90 313 151 0 70 191 121 27 83 47
+ 64 68 173 119 148 0 272 180 315 188 193 245
+ 258 228 29 159 342 209 0 219 83 172 149 79
+ 139 134 112 126 62 199 153 97 0 293 50 232
+ 264 148 232 203 190 248 122 259 227 219 134 0
+ 54 219 92 82 119 31 43 58 238 147 84 53
+ 267 170 255 0 211 74 81 182 105 150 121 108
+ 310 37 160 145 196 99 125 173 0 290 139 98
+ 261 144 176 164 136 389 116 147 224 275 178 154
+ 190 79 0 268 53 138 239 123 207 178 165 367
+ 86 187 202 227 130 68 230 57 86 0 261 43
+ 200 232 98 200 171 131 166 90 227 195 137 69
+ 82 223 90 176 90 0 175 128 76 146 32 76
+ 47 30 222 56 103 109 225 104 164 99 57 112
+ 114 134 0 250 99 89 221 105 189 160 147 349
+ 76 138 184 235 138 114 212 39 40 46 136 96
+ 0 192 228 235 108 119 165 178 154 71 136 262
+ 110 74 96 264 187 182 261 239 165 151 221 0
+ 121 142 99 84 35 29 42 36 220 70 126 55
+ 249 104 178 60 96 175 153 146 47 135 169 0
+EOF