new tsp with minimum, new script to make stats, new png with stats

tests/01_cluster_splitter.py

@@ -58,7 +58,7 @@ def plot_clusters(cities, clusters):
 
 nb_ville = 1000
 max_coords = 1000
-nb_truck = 2
+nb_truck = 20
 
 # Define the coordinates of the cities
 # And set depot at the first city in the middle of the map

tests/101_analyse_aco.py

@@ -1,17 +1,16 @@
 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
+name = "att48"
+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
 
 n_ants = 10
 alpha = 1
 beta = 2
 evaporation = 0.5
 intensification = 2 
-max_times = [1, 2, 5]
+max_times = [0.1, 0.5, 1]
 iterations = 2
 
 best_distances = []
@@ -48,13 +47,13 @@ for max_time in max_times:
         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)
+title += "Best distance per iterations ({})\n".format(name)
+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.xlabel('Iteration')
 plt.ylabel('Distance')
@@ -70,12 +69,16 @@ 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')
+for i, dist in enumerate(best_distances):
+    plt.scatter(times[i], dist[0], color=dist[1])
+    plt.text(times[i], dist[0], "dist: {}\ntime: {}s".format(int(dist[0]), times[i]), rotation=75, va='bottom')
 
-plt.xticks(range(len(values)), [str(i+1) for i in range(len(values))])
+plt.title(title)
+plt.xlabel('Max Time (seconds)')
+plt.ylabel('Distance')
+plt.axhline(y=optimal, color='r')
+
+max_dist = max([x[0] for x in best_distances])  # Extractions des valeurs
+plt.ylim(0, max_dist+max_dist*0.2)
 
 plt.show()

tests/103_analyse_aco_alpha_beta.py

@@ -0,0 +1,43 @@
+import numpy as np
+from matplotlib import pyplot as plt
+from libs.aco import AntColony, total_distance
+
+name = "att48"
+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
+
+n_ants = 10
+alpha = [1, 2]
+beta = [1, 2]
+evaporation = 0.5
+intensification = 2
+max_times = [0.5, 1, 1, 1, 1, 2]
+iterations = 1
+
+bar_width = 0.15
+opacity = 0.8
+
+x = np.arange(len(max_times))
+
+fig, ax = plt.subplots()
+
+for i in range(len(alpha)):
+    for j in range(len(beta)):
+        best_distances = []
+        for max_time in max_times:
+            for iteration in range(iterations):
+                ant_colony = AntColony(cities, n_ants, alpha[i], beta[j], 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))
+        ax.bar(x + (i * len(beta) + j) * bar_width, best_distances, bar_width, alpha=opacity, label='alpha={} beta={}'.format(alpha[i], beta[j]))
+
+ax.set_xlabel('Max Time')
+ax.set_ylabel('Best Distance')
+ax.set_title('Best distances per max time for each alpha and beta')
+ax.set_xticks(x + bar_width / 2)
+ax.set_xticklabels(max_times)
+ax.legend()
+
+plt.axhline(y=optimal, color='r')
+plt.show()

tests/data_sample/34_cities_unbalanced_for_clustering_demo.txt

@@ -0,0 +1 @@
+[[111, 952], [74, 934], [161, 942], [113, 892], [37, 858], [182, 835], [214, 910], [161, 887], [91, 807], [30, 797], [858, 941], [909, 939], [944, 895], [950, 836], [950, 778], [939, 734], [889, 734], [861, 783], [845, 854], [885, 875], [940, 227], [872, 210], [832, 120], [879, 65], [934, 75], [952, 151], [917, 195], [885, 144], [895, 102], [922, 137], [108, 214], [59, 185], [94, 117], [154, 116]]

tests/data_sample/40_cities_balanced_for_clustering_demo.txt

@@ -0,0 +1 @@
+[[904, 878], [767, 923], [878, 779], [815, 853], [778, 744], [694, 794], [874, 129], [783, 161], [809, 53], [870, 52], [818, 101], [749, 69], [165, 115], [106, 172], [91, 80], [208, 35], [191, 155], [126, 105], [104, 709], [176, 831], [95, 906], [203, 753], [254, 825], [125, 829], [841, 794], [244, 817], [143, 96], [860, 50], [752, 844], [210, 808], [117, 48], [862, 124], [183, 59], [143, 177], [876, 219], [928, 97], [726, 822], [791, 894], [110, 861], [120, 767]]

tests/data_sample/48_cities_minimum_33523.txt

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

@@ -1,3 +0,0 @@
-[['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/att48.opt.tour

@@ -0,0 +1,55 @@
+NAME : att48.opt.tour
+COMMENT : Optimum solution  for att48   
+TYPE : TOUR
+DIMENSION : 48
+TOUR_SECTION
+1
+8
+38
+31
+44
+18
+7
+28
+6
+37
+19
+27
+17
+43
+30
+36
+46
+33
+20
+47
+21
+32
+39
+48
+5
+42
+24
+10
+45
+35
+4
+26
+2
+29
+34
+41
+16
+22
+3
+23
+14
+25
+13
+11
+12
+15
+40
+9
+-1
+EOF

tests/data_sample/ALL_tsp/att48.tsp

@@ -0,0 +1,55 @@
+NAME : att48
+COMMENT : 48 capitals of the US (Padberg/Rinaldi)
+TYPE : TSP
+DIMENSION : 48
+EDGE_WEIGHT_TYPE : ATT
+NODE_COORD_SECTION
+1 6734 1453
+2 2233 10
+3 5530 1424
+4 401 841
+5 3082 1644
+6 7608 4458
+7 7573 3716
+8 7265 1268
+9 6898 1885
+10 1112 2049
+11 5468 2606
+12 5989 2873
+13 4706 2674
+14 4612 2035
+15 6347 2683
+16 6107 669
+17 7611 5184
+18 7462 3590
+19 7732 4723
+20 5900 3561
+21 4483 3369
+22 6101 1110
+23 5199 2182
+24 1633 2809
+25 4307 2322
+26 675 1006
+27 7555 4819
+28 7541 3981
+29 3177 756
+30 7352 4506
+31 7545 2801
+32 3245 3305
+33 6426 3173
+34 4608 1198
+35 23 2216
+36 7248 3779
+37 7762 4595
+38 7392 2244
+39 3484 2829
+40 6271 2135
+41 4985 140
+42 1916 1569
+43 7280 4899
+44 7509 3239
+45 10 2676
+46 6807 2993
+47 5185 3258
+48 3023 1942
+EOF

tests/data_sample/att48_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/berlin52_cities_minimum_7542.txt

@@ -0,0 +1,3 @@
+[[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]]

tests/data_sample/eil76_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/libs/aco.py

@@ -20,7 +20,7 @@ class AntColony:
 
     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 / self.distance(self.cities[ant[-1]], self.cities[i])) ** self.beta) for i in unvisited_cities]
+        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]
@@ -29,7 +29,7 @@ class AntColony:
         return np.random.choice(unvisited_cities, p=probabilities)
 
     def update_pheromones(self, ant):
-        pheromones_delta = self.intensification / self.total_distance([self.cities[i] for i in 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
 
@@ -42,7 +42,7 @@ class AntColony:
             for ant in ants:
                 for _ in range(self.n - 1):
                     ant.append(self.choose_next_city(ant))
-                ant_distance = self.total_distance([self.cities[i] for i in 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()