adding hybrid resolution

tests/05_cluster_ant_colony_recuit_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]
+
+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()