script for stats + stats

tests/04_cluster_ant_colony_no_animation.py

@@ -11,13 +11,15 @@ def generate_cities(nb, max_coords=1000):
 nb_ville = 2000
 max_coords = 1000
 nb_truck = 1
-max_time = 2
+max_time = 5
 nb_ants = 10
 
 max_time_per_cluster = max_time / nb_truck
 
 start_time_generate = time.time()
-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]]
+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]]
 #cities[0] = [max_coords/2, max_coords/2]
 stop_time_generate = time.time()
 
@@ -84,8 +86,8 @@ for i, (route, color) in enumerate(best_routes):
     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.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 = {nb_ville}, nb_truck = {nb_truck}, max_time = {max_time}")   
+    plt.title(f"nb_ville = {len(cities)}, nb_truck = {nb_truck}, max_time = {max_time}")   
 
 plt.show()

tests/04_cluster_ant_colony_no_animation_no_random_alpha_variation.py

@@ -0,0 +1,79 @@
+import matplotlib.pyplot as plt
+import numpy as np
+from libs.clustering import split_tour_across_clusters
+from libs.aco import AntColony, total_distance
+
+cities = [[565, 575], [25, 185], [345, 750], [945, 685], [845, 655], [880, 660], [25, 230], [525, 1000], [580, 1175], [650, 1130], [1605, 620], [1220, 580], [1465, 200], [1530, 5], [845, 680], [725, 370], [145, 665], [415, 635], [510, 875], [560, 365], [300, 465], [520, 585], [480, 415], 
+[835, 625], [975, 580], [1215, 245], [1320, 315], [1250, 400], [660, 180], [410, 250], [420, 555], [575, 665], [1150, 1160], [700, 580], [685, 595], [685, 610], [770, 610], [795, 645], [720, 635], [760, 650], [475, 960], [95, 260], [875, 920], [700, 500], [555, 815], [830, 485], 
+[1170, 65], [830, 610], [605, 625], [595, 360], [1340, 725], [1740, 245]]
+optimal = 7542
+
+max_times = [0.01, 0.1, 0.2, 0.5]
+n_ants = 10
+alphas = [1, 4]
+beta = 4
+evaporation = 0.5
+intensification = 2
+n_runs = 5  # Nombre de fois où chaque configuration sera exécutée pour obtenir une moyenne
+
+average_best_route_per_max_time_per_alpha = []
+
+for alpha in alphas:
+    average_best_route_per_max_time = []
+    for max_time in max_times:
+
+        total_best_route_length = 0
+
+        for _ in range(n_runs):
+            print("Run number: ", _, "for max_time: ", max_time)
+            clusters = split_tour_across_clusters(cities, 1)
+
+            total_distance_for_run = 0
+
+            for i, cluster_indices in enumerate(clusters.values()):
+                cluster_cities = [cities[index] for index in cluster_indices]
+
+                ant_colony = AntColony(cluster_cities, n_ants=n_ants, max_time=max_time, alpha=alpha, beta=beta, evaporation=evaporation, intensification=intensification)
+                best_route = ant_colony.run()
+                total_distance_for_run += total_distance(best_route)
+
+            total_best_route_length += total_distance_for_run
+
+        average_best_route_length = total_best_route_length / n_runs
+        average_best_route_per_max_time.append(average_best_route_length)
+
+    average_best_route_per_max_time_per_alpha.append(average_best_route_per_max_time)
+
+colors = ['b', 'g']
+
+total_bars_per_group = len(alphas)
+total_groups = len(max_times)
+
+unit_space_per_group = 1
+bars_space_per_group = total_bars_per_group / (total_bars_per_group + 1)
+bar_width = bars_space_per_group / total_bars_per_group
+
+bar_positions = np.arange(total_groups)
+
+plt.figure(figsize=(10, 7))
+
+for i, average_best_route_per_max_time in enumerate(average_best_route_per_max_time_per_alpha):
+    plt.bar(bar_positions + i * bar_width, average_best_route_per_max_time, width=bar_width, color=colors[i])
+
+plt.axhline(y=optimal, color='r')
+
+plt.xticks(bar_positions + bar_width / 2, max_times)  # Set the x-axis labels to be the max_time values
+plt.xlabel('Max time')
+plt.ylabel('Average best route length')
+
+title = ""
+title += "Average best route length ({} iterations) for different max times\n".format(n_runs)
+title += "Nb cities: " + str(len(cities)) + " / "
+title += "Ants: " + str(n_ants) + " / "
+title += "Alpha: " + str(alphas) + " / "
+title += "Beta: " + str(beta) + " / "
+title += "Evaporation: " + str(evaporation) + " / "
+title += "Intensification: " + str(intensification)
+plt.title(title)
+
+plt.show()

tests/04_cluster_ant_colony_no_animation_no_random_beta_variation.py

@@ -0,0 +1,86 @@
+import matplotlib.pyplot as plt
+from libs.clustering import split_tour_across_clusters
+from libs.aco import AntColony, total_distance
+import numpy as np
+
+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 = [0.1, 0.2, 0.5, 1, 2]
+n_ants = 10
+alpha = 1
+betas = [2, 4, 6, 8]  # Pour contenir deux valeurs de beta
+evaporation = 0.5
+intensification = 2
+n_runs = 2  # Nombre de fois où chaque configuration sera exécutée pour obtenir une moyenne
+
+average_best_route_per_max_time_per_beta = []
+
+for beta in betas:
+    average_best_route_per_max_time = []
+
+    for max_time in max_times:
+
+        total_best_route_length = 0
+
+        for _ in range(n_runs):
+            print("Run number: ", _, "for max_time: ", max_time, " and beta: ", beta)
+            clusters = split_tour_across_clusters(cities, 1)
+
+            total_distance_for_run = 0
+
+            for i, cluster_indices in enumerate(clusters.values()):
+                cluster_cities = [cities[index] for index in cluster_indices]
+
+                ant_colony = AntColony(cluster_cities, n_ants=n_ants, max_time=max_time, alpha=alpha, beta=beta, evaporation=evaporation, intensification=intensification)
+                best_route = ant_colony.run()
+                total_distance_for_run += total_distance(best_route)
+
+            total_best_route_length += total_distance_for_run
+
+        average_best_route_length = total_best_route_length / n_runs
+        average_best_route_per_max_time.append(average_best_route_length)
+
+    average_best_route_per_max_time_per_beta.append(average_best_route_per_max_time)
+
+# Maintenant, nous avons toutes les valeurs moyennes, créons un histogramme
+colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
+
+total_bars_per_group = len(betas)
+total_groups = len(max_times)
+
+# Unité d'espace pour chaque groupe de barres (incluant l'espace entre les groupes)
+unit_space_per_group = 1
+
+# Espace qui serait occupé par les barres dans chaque groupe
+bars_space_per_group = total_bars_per_group / (total_bars_per_group + 1)
+
+# La largeur de chaque barre serait
+bar_width = bars_space_per_group / total_bars_per_group
+index = np.arange(len(max_times))
+
+plt.figure()
+
+for i, average_best_route_per_max_time in enumerate(average_best_route_per_max_time_per_beta):
+    plt.bar(index + i * bar_width, average_best_route_per_max_time, bar_width, color=colors[i], label='Beta = '+str(betas[i]))
+    for j, v in enumerate(average_best_route_per_max_time):
+        plt.text(j + i * bar_width, v + 0.01 + (i*0.2), int(v), va='bottom', ha='center')
+
+plt.axhline(y=optimal, color='r')
+plt.xlabel('Max time')
+plt.ylabel('Average best route length')
+
+title = ""
+title += "Comparaison of betas with average best route length ({} iterations) for different max times and each betas\n".format(n_runs)
+title += "Nb cities: " + str(len(cities)) + " / "
+title += "Ants: " + str(n_ants) + " / "
+title += "Alpha: " + str(alpha) + " / "
+title += "Evaporation: " + str(evaporation) + " / "
+title += "Intensification: " + str(intensification)
+plt.title(title)
+
+plt.xticks(index + bar_width / 2, max_times)
+plt.legend()
+
+plt.tight_layout()
+plt.show()

tests/04_cluster_ant_colony_no_animation_no_random_single_value.py

@@ -0,0 +1,91 @@
+import matplotlib.pyplot as plt
+from libs.clustering import split_tour_across_clusters
+from libs.aco import AntColony, total_distance
+
+cities = [[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]]
+optimal = 538
+
+max_times = [0.01, 0.1, 0.2, 0.5, 1, 2, 5, 10, 20]
+n_ants = 10
+alpha = 1
+beta = 4
+evaporation = 0.5
+intensification = 2
+n_runs = 5  # Nombre de fois où chaque configuration sera exécutée pour obtenir une moyenne
+
+
+average_best_route_per_max_time = []
+
+for max_time in max_times:
+
+    total_best_route_length = 0
+
+    for _ in range(n_runs):
+        print("Run number: ", _, "for max_time: ", max_time)
+        clusters = split_tour_across_clusters(cities, 1)
+
+        total_distance_for_run = 0
+
+        for i, cluster_indices in enumerate(clusters.values()):
+            cluster_cities = [cities[index] for index in cluster_indices]
+
+            ant_colony = AntColony(cluster_cities, n_ants=n_ants, max_time=max_time, alpha=alpha, beta=beta, evaporation=evaporation, intensification=intensification)
+            best_route = ant_colony.run()
+            total_distance_for_run += total_distance(best_route)
+
+        total_best_route_length += total_distance_for_run
+
+    average_best_route_length = total_best_route_length / n_runs
+    average_best_route_per_max_time.append(average_best_route_length)
+
+# Maintenant, nous avons toutes les valeurs moyennes, créons un histogramme
+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
+]
+
+plt.figure()
+bar_width = 0.8
+bar_positions = range(len(max_times))  # Crée une liste d'indices pour chaque barre
+plt.bar(bar_positions, average_best_route_per_max_time, width=bar_width, color=colors[:len(max_times)])
+plt.axhline(y=optimal, color='r')
+
+# Ajouter des valeurs au-dessus des barres
+for i, v in enumerate(average_best_route_per_max_time):
+    plt.text(i - 0.15, v + 0.01, round(v, 2))
+
+plt.xticks(bar_positions, max_times)  # Fixe les labels sur l'axe des x aux valeurs de max_time
+plt.xlabel('Max time')
+plt.ylabel('Average best route length')
+
+title = ""
+title += "Average best route length ({} iterations) for different max times\n".format(n_runs)
+title += "Nb cities: " + str(len(cities)) + " / "
+title += "Ants: " + str(n_ants) + " / "
+title += "Alpha: " + str(alpha) + " / "
+title += "Beta: " + str(beta) + " / "
+title += "Evaporation: " + str(evaporation) + " / "
+title += "Intensification: " + str(intensification)
+plt.title(title)
+
+plt.show()

tests/data_sample/ALL_tsp/ch150.opt.tour

@@ -0,0 +1,157 @@
+NAME : ch150.opt.tour
+COMMENT : Length 6528
+TYPE : TOUR
+DIMENSION : 150
+TOUR_SECTION
+1
+98
+103
+82
+95
+107
+5
+100
+143
+97
+146
+26
+75
+18
+142
+85
+65
+132
+137
+50
+55
+58
+141
+83
+56
+90
+46
+92
+54
+138
+134
+131
+32
+23
+38
+67
+43
+109
+51
+20
+25
+110
+81
+29
+86
+135
+70
+108
+102
+114
+99
+19
+2
+37
+6
+28
+9
+42
+120
+47
+139
+40
+53
+118
+24
+12
+116
+101
+41
+57
+39
+127
+69
+36
+61
+11
+148
+130
+17
+66
+60
+140
+117
+129
+27
+31
+123
+74
+13
+106
+91
+119
+68
+128
+45
+71
+44
+64
+112
+136
+145
+144
+49
+147
+72
+80
+14
+122
+77
+133
+15
+78
+21
+150
+115
+4
+104
+22
+125
+149
+62
+3
+113
+10
+94
+88
+121
+79
+59
+16
+111
+105
+33
+126
+52
+93
+124
+35
+96
+89
+8
+7
+84
+30
+63
+48
+73
+76
+34
+87
+-1
+EOF

tests/data_sample/ALL_tsp/eil76.opt.tour

@@ -0,0 +1,83 @@
+NAME : eil76.opt.tour
+COMMENT : Optimum tour for eil76.tsp (538)
+TYPE : TOUR
+DIMENSION : 76
+TOUR_SECTION
+1
+33
+63
+16
+3
+44
+32
+9
+39
+72
+58
+10
+31
+55
+25
+50
+18
+24
+49
+23
+56
+41
+43
+42
+64
+22
+61
+21
+47
+36
+69
+71
+60
+70
+20
+37
+5
+15
+57
+13
+54
+19
+14
+59
+66
+65
+38
+11
+53
+7
+35
+8
+46
+34
+52
+27
+45
+29
+48
+30
+4
+75
+76
+67
+26
+12
+40
+17
+51
+6
+68
+2
+74
+28
+62
+73
+-1
+EOF