Merge branch 'Test'

.vscode/settings.json

@@ -0,0 +1,5 @@
+{
+    "python.analysis.extraPaths": [
+        "./tests"
+    ]
+}

Projet_algo_french.ipynb

@@ -228,7 +228,7 @@
    "id": "e635cf14",
    "metadata": {},
    "source": [
-    "#### **Fonction objectif**"
+    "#### **Fonction objective**"
    ]
   },
   {

tests/03_cluster_recuit_no_animation_no_random_colling_rate_variation.py

@@ -30,8 +30,6 @@ 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

tests/04_cluster_ant_colony_no_animation_elbow_algo.py

@@ -42,70 +42,3 @@ plt.xlabel('Number of clusters')
 plt.ylabel('Inertia')
 plt.show()
 
-
-
-
-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, alpha=1, beta=5)
-    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="blue", marker='o', linestyle='-', label=f"Cluster {i}")
-    # add title with nb_ville, nb_truck and max_time
-    plt.title(f"nb_ville = {len(cities)}, nb_truck = {nb_truck}, max_time = {max_time}")   
-
-plt.show()

tests/101_analyse_ant_colony_no_animation_no_random_single_value.py

@@ -2,15 +2,13 @@ import matplotlib.pyplot as plt
 from libs.clustering import split_tour_across_clusters
 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
+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
 
-max_times = [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20]
+max_times = [0.01, 0.1, 0.2, 0.5]
 n_ants = 10
 alpha = 1
-beta = 6
+beta = 4
 evaporation = 0.5
 intensification = 2
 n_runs = 3  # Nombre de fois où chaque configuration sera exécutée pour obtenir une moyenne

tests/libs/pso.py

@@ -0,0 +1,74 @@
+# Import necessary libraries
+import numpy as np
+from sklearn.cluster import KMeans
+
+# Class to represent a Particle in Particle Swarm Optimization
+class Particle:
+    def __init__(self, num_cities, num_trucks, distances, time_windows, infinite_distance_value=1e6):
+        # Initialize the particle's position, velocity, and best position
+        self.position = np.random.permutation(range(1, num_cities+1))
+        self.velocity = np.zeros_like(self.position)
+        self.best_position = self.position.copy()
+        self.best_cost = float('inf')
+        self.num_trucks = num_trucks
+        self.distances = distances
+        self.time_windows = time_windows
+        self.infinite_distance_value = infinite_distance_value
+
+        # Cluster the cities based on their distances
+        self.city_cluster = self.cluster_cities()
+
+    # Function to cluster cities based on their distances
+    def cluster_cities(self):
+        # Replace infinite distances with a large finite value
+        clustering_distances = np.where(self.distances == float('inf'), self.infinite_distance_value, self.distances)
+        kmeans = KMeans(n_clusters=self.num_trucks, n_init=10)
+        clusters = kmeans.fit_predict(clustering_distances)
+        return clusters
+
+    # Function to evaluate the cost of the particle's current position
+    def evaluate_cost(self):
+        # Split cities among trucks
+        trucks = [[] for _ in range(self.num_trucks)]
+        for i in range(len(self.position)):
+            current_city = int(self.position[i])
+            truck = self.city_cluster[current_city - 1]
+            trucks[truck].append(current_city)
+
+        # Calculate the total cost based on the schedule of each truck
+        total_cost = 0
+        for truck in trucks:
+            if len(truck) > 0:
+                departure_time = self.time_windows[truck[0] - 1][0]
+                for i in range(len(truck) - 1):
+                    u, v = int(truck[i]), int(truck[i+1])
+                    distance = self.distances[u-1, v-1] if self.distances[u-1, v-1] != float('inf') else self.infinite_distance_value
+                    total_cost += distance
+                    arrival_time = departure_time + distance
+                    time_window = self.time_windows[v-1]
+                    if arrival_time < time_window[0]:
+                        total_cost += time_window[0] - arrival_time
+                        departure_time = time_window[0]
+                    elif arrival_time > time_window[1]:
+                        total_cost += arrival_time - time_window[1]
+                        departure_time = arrival_time
+                    else:
+                        departure_time = arrival_time
+        return total_cost
+
+    # Function to update the particle's velocity
+    def update_velocity(self, global_best_position, inertia_weight, cognitive_weight, social_weight):
+        r1 = np.random.random()
+        r2 = np.random.random()
+        cognitive_component = cognitive_weight * r1 * (self.best_position - self.position)
+        social_component = social_weight * r2 * (global_best_position - self.position)
+        self.velocity = inertia_weight * self.velocity + cognitive_component + social_component
+
+    # Function to update the particle's position based on its velocity
+    def update_position(self):
+        indices = np.random.choice(range(len(self.position)), 2, replace=False)
+        self.position[indices[0]], self.position[indices[1]] = self.position[indices[1]], self.position[indices[0]]
+        current_cost = self.evaluate_cost()
+        if current_cost < self.best_cost:
+            self.best_cost = current_cost
+            self.best_position = self.position.copy()

tests/libs/simulated_annealing.py

@@ -14,7 +14,6 @@ class SimulatedAnnealing:
         self.temperature_ok = temperature_ok
 
     def run(self):
-        interration = 0
         current_solution = self.cities.copy()
         best_solution = self.cities.copy()
         while self.temperature > self.temperature_ok:
@@ -33,8 +32,4 @@ class SimulatedAnnealing:
                     best_solution = current_solution
             # Cool down
             self.temperature *= self.cooling_rate
-            interration += 1
-            # Print every 1000 iterations
-            if interration % 1000 == 0:
-                print("Iteration", interration, "with distance", total_distance(current_solution))
         return best_solution

tests/libs/simulated_annealing_stats.py

@@ -40,7 +40,4 @@ class SimulatedAnnealing:
             # Cool down
             self.temperature *= self.cooling_rate
             interration += 1
-            # Print every 1000 iterations
-            if interration % 10 == 0:
-                print("Iteration", interration, "with distance", total_distance(current_solution))
         return best_solution, self.distances