diff --git a/Projet_algo.ipynb b/Projet_algo.ipynb index a33321d..65606f7 100644 --- a/Projet_algo.ipynb +++ b/Projet_algo.ipynb @@ -267,7 +267,7 @@ "#### **VRPTW constraints**\n", "\n", "- Each city is visited once and only once:\n", - "βˆ‘π‘˜βˆˆπΎβˆ‘π‘—βˆˆπ‘‰π‘₯π‘–π‘—π‘˜=1, βˆ€π‘–βˆˆπ‘‰, 𝑖≠0\n", + "$$\\sum_{k \\in K} \\sum_{j \\in V}x_{ijk} = 1, \\forall i \\in V, i \\ne 0 $$\n", "\n", "- If a truck visits a city, it must leave it:\n", "βˆ‘π‘–βˆˆπ‘‰π‘₯π‘–π‘—π‘˜=βˆ‘π‘—βˆˆπ‘‰π‘₯π‘–π‘—π‘˜, βˆ€π‘˜βˆˆπΎ, βˆ€π‘–βˆˆπ‘‰, βˆ€π‘—βˆˆπ‘‰\n", diff --git a/tests/02_cluster_recuit_live_animation.py b/tests/02_cluster_recuit_live_animation.py index 3f6e0e0..6f6102e 100644 --- a/tests/02_cluster_recuit_live_animation.py +++ b/tests/02_cluster_recuit_live_animation.py @@ -9,6 +9,7 @@ def generate_cities(nb, max_coords=1000): return [random.sample(range(max_coords), 2) for _ in range(nb)] previous_route = None +random.seed(42) def draw_cities(cities, previous_route, color='b', title=' '): plt.title(title) @@ -59,9 +60,9 @@ def simulated_annealing(cities, color='b', temperature=100000, cooling_rate=0.99 plt.ioff() return best_solution -nb_ville = 50 +nb_ville = 150 max_coords = 1000 -nb_truck = 4 +nb_truck = 3 temperature = 10000 cooling_rate = 0.999 temperature_ok = 0.000001 diff --git a/tests/03_cluster_recuit_no_animation.py b/tests/03_cluster_recuit_no_animation.py index 16f4b0a..8717ff2 100644 --- a/tests/03_cluster_recuit_no_animation.py +++ b/tests/03_cluster_recuit_no_animation.py @@ -3,14 +3,16 @@ import random, time from libs.clustering import split_tour_across_clusters from libs.simulated_annealing import SimulatedAnnealing, total_distance +random.seed(42) + def generate_cities(nb, max_coords=1000): return [random.sample(range(max_coords), 2) for _ in range(nb)] -nb_ville = 100 +nb_ville = 150 max_coords = 1000 -nb_truck = 4 +nb_truck = 3 temperature = 10000 -cooling_rate = 0.999 +cooling_rate = 0.9999 temperature_ok = 0.001 start_time_generate = time.time() diff --git a/tests/04_cluster_ant_colony_no_animation.py b/tests/04_cluster_ant_colony_no_animation.py index bb03961..adc4416 100644 --- a/tests/04_cluster_ant_colony_no_animation.py +++ b/tests/04_cluster_ant_colony_no_animation.py @@ -5,22 +5,26 @@ import random, time, math from libs.clustering import split_tour_across_clusters from libs.aco import AntColony, total_distance +random.seed(42) + def generate_cities(nb, max_coords=1000): return [random.sample(range(max_coords), 2) for _ in range(nb)] -nb_ville = 2000 +nb_ville = 150 max_coords = 1000 -nb_truck = 1 -max_time = 10 +nb_truck = 3 +max_time = 6 nb_ants = 10 +alpha = 1 +beta = 6 +evaporation = 0.5 +intensification = 2 max_time_per_cluster = max_time / nb_truck start_time_generate = time.time() -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] +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() @@ -68,7 +72,7 @@ for i, cluster_indices in enumerate(clusters.values()): 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) + ant_colony = AntColony(cluster_cities, n_ants=nb_ants, max_time=max_time_per_cluster, alpha=alpha, beta=beta, evaporation=evaporation, intensification=intensification) best_route = ant_colony.run() best_routes.append((best_route, color)) @@ -86,7 +90,7 @@ 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="blue", marker='o', linestyle='-', label=f"Cluster {i}") + plt.plot(x, y, color=color, 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}") diff --git a/tests/102_analyse_simulated_annealing_no_random_temperatures_variations.py b/tests/102_analyse_simulated_annealing_no_random_temperatures_variations.py index ab1bdd2..f24c5ae 100644 --- a/tests/102_analyse_simulated_annealing_no_random_temperatures_variations.py +++ b/tests/102_analyse_simulated_annealing_no_random_temperatures_variations.py @@ -3,6 +3,7 @@ from libs.clustering import split_tour_across_clusters from libs.simulated_annealing import SimulatedAnnealing, total_distance import numpy as np +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 @@ -32,10 +33,10 @@ colors = [ nb_ville = 100 max_coords = 1000 nb_truck = 1 -temperatures = [1000, 10000, 100000,] -cooling_rate = 0.999 +temperatures = [100 ,1000, 10000, 100000] +cooling_rate = 0.9999 temperature_ok = 0.001 -iterations = 1 +iterations = 3 clusters = split_tour_across_clusters(cities, nb_truck) @@ -110,9 +111,18 @@ for i in range(len(temperatures)): for i in range(len(temperatures)): plt.text(i, average_distances[i], round(average_distances[i], 2), ha = 'center') +# DΓ©finir un titre +title = "" +title += "Average distance per temperature ({})\n".format(name) +title += "Nb cities: " + str(len(cities)) + " / " +title += "Cooling rate: " + str(cooling_rate) + " / " +title += "Temperature ok: " + str(temperature_ok) +plt.title(title) +plt.axhline(y=optimal, color='r') + # DΓ©finir les Γ©tiquettes des axes -plt.xlabel('TempΓ©rature initiale') -plt.ylabel('Moyenne des distances sur {} itΓ©rations'.format(iterations)) +plt.xlabel('Initial temperature') +plt.ylabel('Average distance over {} iterations'.format(iterations)) # Afficher l'histogramme plt.show() \ No newline at end of file