making libs and python program to test algorithms. Updating 01 to 05 to use libs
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Louis
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.assets/images/stats_52berlin_aco_0.1_to_120_secs.png
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.assets/images/stats_52berlin_aco_0.1_to_120_secs.png
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@ -58,7 +58,7 @@ def plot_clusters(cities, clusters):
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nb_ville = 1000
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max_coords = 1000
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nb_truck = 20
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nb_truck = 2
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# Define the coordinates of the cities
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# And set depot at the first city in the middle of the map
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@ -3,18 +3,11 @@ import matplotlib.pyplot as plt
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import numpy as np
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import random, time, math
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from libs.clustering import split_tour_across_clusters
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random.seed(1)
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from libs.simulated_annealing import total_distance
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def generate_cities(nb, max_coords=1000):
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return [random.sample(range(max_coords), 2) for _ in range(nb)]
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def distance(city1, city2):
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return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)
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def total_distance(cities):
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return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
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previous_route = None
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def draw_cities(cities, previous_route, color='b', title=' '):
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@ -66,7 +59,7 @@ def simulated_annealing(cities, color='b', temperature=100000, cooling_rate=0.99
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plt.ioff()
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return best_solution
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nb_ville = 200
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nb_ville = 50
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max_coords = 1000
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nb_truck = 4
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temperature = 10000
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@ -1,48 +1,12 @@
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from sklearn.cluster import KMeans
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import matplotlib.pyplot as plt
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import numpy as np
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import random, time, math
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import random, time
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from libs.clustering import split_tour_across_clusters
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from libs.simulated_annealing import SimulatedAnnealing, total_distance
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def generate_cities(nb, max_coords=1000):
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return [random.sample(range(max_coords), 2) for _ in range(nb)]
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def distance(city1, city2):
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return math.sqrt((city1[0] - city2[0]) ** 2 + (city1[1] - city2[1]) ** 2)
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def total_distance(cities):
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return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
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previous_route = None
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def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001, cluster_index=0):
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interration = 0
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current_solution = cities.copy()
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best_solution = cities.copy()
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while temperature > temperature_ok:
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new_solution = current_solution.copy()
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# Swap two cities in the route
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i = random.randint(0, len(new_solution) - 1)
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j = random.randint(0, len(new_solution) - 1)
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new_solution[i], new_solution[j] = new_solution[j], new_solution[i]
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# Calculate the acceptance probability
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current_energy = total_distance(current_solution)
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new_energy = total_distance(new_solution)
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delta = new_energy - current_energy
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if delta < 0 or random.random() < math.exp(-delta / temperature):
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current_solution = new_solution
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if total_distance(current_solution) < total_distance(best_solution):
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best_solution = current_solution
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# Cool down
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temperature *= cooling_rate
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interration += 1
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# Print every 1000 iterations
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if interration % 1000 == 0:
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print("Cluster", cluster_index, ": iteration", interration, "with current total distance", total_distance(current_solution))
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return best_solution
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nb_ville = 20
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nb_ville = 100
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max_coords = 1000
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nb_truck = 4
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temperature = 10000
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@ -99,7 +63,8 @@ for i, cluster_indices in enumerate(clusters.values()):
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cluster_cities = [cities[index] for index in cluster_indices]
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# Appel de la fonction simulated_annealing
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best_route = simulated_annealing(cluster_cities, temperature, cooling_rate, temperature_ok)
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simulated_annealing = SimulatedAnnealing(cluster_cities, temperature=10000, cooling_rate=0.999, temperature_ok=0.01)
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best_route = simulated_annealing.run()
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best_routes.append((best_route, color))
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print("Final solution for cluster ", i, ":", best_route)
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81
tests/101_analyse_aco.py
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81
tests/101_analyse_aco.py
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@ -0,0 +1,81 @@
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from matplotlib import pyplot as plt
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from libs.aco import AntColony, total_distance
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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,
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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,
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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]]
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optimal = 6528
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n_ants = 10
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alpha = 1
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beta = 2
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evaporation = 0.5
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intensification = 2
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max_times = [1, 2, 5]
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iterations = 2
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best_distances = []
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times = []
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colors = [
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'#1f77b4', # Bleu moyen
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'#ff7f0e', # Orange
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'#2ca02c', # Vert
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'#d62728', # Rouge
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'#9467bd', # Violet
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'#8c564b', # Marron
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'#e377c2', # Rose
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'#7f7f7f', # Gris
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'#bcbd22', # Vert olive
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'#17becf', # Turquoise
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'#1b9e77', # Vert Teal
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'#d95f02', # Orange foncé
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'#7570b3', # Violet moyen
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'#e7298a', # Fuchsia
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'#66a61e', # Vert pomme
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'#e6ab02', # Jaune or
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'#a6761d', # Bronze
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'#666666', # Gris foncé
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'#f781bf', # Rose clair
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'#999999', # Gris moyen
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]
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for max_time in max_times:
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for iteration in range(iterations):
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ant_colony = AntColony(cities, n_ants, alpha, beta, evaporation, intensification, max_time)
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print("Running iteration number {}/{} ({} sec)".format(iteration + 1, iterations, max_time))
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best_route = ant_colony.run()
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best_distances.append([total_distance(best_route), colors[max_times.index(max_time) % len(colors)]])
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times.append(max_time)
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title = ""
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title += "Best distance per iterations\n"
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title += "Ants: " + str(n_ants) + " "
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title += "Alpja: " + str(alpha) + " "
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title += "Beta: " + str(beta) + " "
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title += "Evaporation: " + str(evaporation) + " "
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title += "Intensification: " + str(intensification) + " "
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title += "Max time: " + str(max_time)
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plt.title(title)
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plt.xlabel('Iteration')
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plt.ylabel('Distance')
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plt.axhline(y=optimal, color='r')
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distances = [x[0] for x in best_distances] # Extractions des valeurs
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max_dist = max(distances)
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plt.ylim(0, max_dist+max_dist*0.2)
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values = [item[0] for item in best_distances]
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colors = [item[1] for item in best_distances]
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bars = plt.bar(range(len(values)), values, color=colors)
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for i, bar in enumerate(bars):
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yval = bar.get_height()
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plt.text(bar.get_x() + bar.get_width()/2, yval + 0.05,
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"dist: {}\ntime: {}s".format(int(yval), times[i]),
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rotation=75, ha='center', va='bottom')
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plt.xticks(range(len(values)), [str(i+1) for i in range(len(values))])
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plt.show()
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76
tests/102_analyse_simulated_annealing.py
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76
tests/102_analyse_simulated_annealing.py
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@ -0,0 +1,76 @@
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from matplotlib import pyplot as plt
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from libs.simulated_annealing import SimulatedAnnealing, distance, total_distance
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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]]
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optimal = 7542
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temperature = 10000
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cooling_rate = 0.999
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temperature_ok = 0.01
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max_times = [1, 2, 5]
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iterations = 2
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best_distances = []
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times = []
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colors = [
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'#1f77b4', # Bleu moyen
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'#ff7f0e', # Orange
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'#2ca02c', # Vert
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'#d62728', # Rouge
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'#9467bd', # Violet
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'#8c564b', # Marron
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'#e377c2', # Rose
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'#7f7f7f', # Gris
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'#bcbd22', # Vert olive
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'#17becf', # Turquoise
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'#1b9e77', # Vert Teal
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'#d95f02', # Orange foncé
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'#7570b3', # Violet moyen
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'#e7298a', # Fuchsia
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'#66a61e', # Vert pomme
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'#e6ab02', # Jaune or
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'#a6761d', # Bronze
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'#666666', # Gris foncé
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'#f781bf', # Rose clair
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'#999999', # Gris moyen
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]
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for max_time in max_times:
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for iteration in range(iterations):
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simulated_annealing = SimulatedAnnealing(cities, temperature=10000, cooling_rate=0.999, temperature_ok=0.01)
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print("Running iteration number {}/{} ({} sec)".format(iteration + 1, iterations, max_time))
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best_distance, best_route = SimulatedAnnealing.run()
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best_distances.append([best_distance, colors[max_times.index(max_time) % len(colors)]])
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times.append(max_time)
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title = ""
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title += "Best distance per iterations\n"
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title += "Temperature: " + str(temperature) + " "
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title += "Cooling rate: " + str(cooling_rate) + " "
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title += "Temperature ok: " + str(temperature_ok) + " "
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plt.title(title)
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plt.xlabel('Iteration')
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plt.ylabel('Distance')
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plt.axhline(y=optimal, color='r')
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distances = [x[0] for x in best_distances] # Extractions des valeurs
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for best_distance in best_distances:
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print(best_distance)
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max_dist = max(distances)
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plt.ylim(0, max_dist+max_dist*0.2)
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values = [item[0] for item in best_distances]
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colors = [item[1] for item in best_distances]
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bars = plt.bar(range(len(values)), values, color=colors)
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for i, bar in enumerate(bars):
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yval = bar.get_height()
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plt.text(bar.get_x() + bar.get_width()/2, yval + 0.05,
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"dist: {}\ntime: {}s".format(int(yval), times[i]),
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rotation=75, ha='center', va='bottom')
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plt.xticks(range(len(values)), [str(i+1) for i in range(len(values))])
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plt.show()
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@ -1 +0,0 @@
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[[-0.0, 0.0], [-21.5, -7.3], [-28.9, -0.0], [-43.1, -14.6], [-50.5, -7.4], [-64.7, -21.9], [-72.1, -0.2], [-79.3, 21.4], [-65.1, 36.1], [-57.6, 43.3], [-50.6, 21.6], [-36.0, 21.6], [-29.1, 43.2], [-14.7, 43.4], [-0.1, 28.7], [-0.0, 0.0]]
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@ -20,7 +20,7 @@ class AntColony:
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def choose_next_city(self, ant):
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unvisited_cities = [i for i in range(self.n) if i not in ant]
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probabilities = [self.pheromones[ant[-1]][i] ** self.alpha * ((1 / distance(self.cities[ant[-1]], self.cities[i])) ** self.beta) for i in unvisited_cities]
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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]
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total = sum(probabilities)
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if total == 0:
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probabilities = [1 / len(unvisited_cities) for _ in unvisited_cities]
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@ -29,7 +29,7 @@ class AntColony:
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return np.random.choice(unvisited_cities, p=probabilities)
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def update_pheromones(self, ant):
|
||||
pheromones_delta = self.intensification / total_distance([self.cities[i] for i in ant])
|
||||
pheromones_delta = self.intensification / self.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 = total_distance([self.cities[i] for i in ant])
|
||||
ant_distance = self.total_distance([self.cities[i] for i in ant])
|
||||
if ant_distance < best_distance:
|
||||
best_distance = ant_distance
|
||||
best_ant = ant.copy()
|
||||
|
||||
@ -6,11 +6,19 @@ def distance(city1, city2):
|
||||
def total_distance(cities):
|
||||
return sum([distance(cities[i - 1], cities[i]) for i in range(len(cities))])
|
||||
|
||||
def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001, cluster_index=0):
|
||||
class SimulatedAnnealing:
|
||||
def __init__(self, cities, temperature=10000, cooling_rate=0.9999, temperature_ok=0.001, cluster_index=0):
|
||||
self.cities = cities
|
||||
self.temperature = temperature
|
||||
self.cooling_rate = cooling_rate
|
||||
self.temperature_ok = temperature_ok
|
||||
self.cluster_index = cluster_index
|
||||
|
||||
def run(self):
|
||||
interration = 0
|
||||
current_solution = cities.copy()
|
||||
best_solution = cities.copy()
|
||||
while temperature > temperature_ok:
|
||||
current_solution = self.cities.copy()
|
||||
best_solution = self.cities.copy()
|
||||
while self.temperature > self.temperature_ok:
|
||||
new_solution = current_solution.copy()
|
||||
# Swap two cities in the route
|
||||
i = random.randint(0, len(new_solution) - 1)
|
||||
@ -20,14 +28,14 @@ def simulated_annealing(cities, temperature=10000, cooling_rate=0.9999, temperat
|
||||
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):
|
||||
if delta < 0 or random.random() < math.exp(-delta / self.temperature):
|
||||
current_solution = new_solution
|
||||
if total_distance(current_solution) < total_distance(best_solution):
|
||||
best_solution = current_solution
|
||||
# Cool down
|
||||
temperature *= cooling_rate
|
||||
self.temperature *= self.cooling_rate
|
||||
interration += 1
|
||||
# Print every 1000 iterations
|
||||
if interration % 1000 == 0:
|
||||
print("Cluster", cluster_index, ": iteration", interration, "with current total distance", total_distance(current_solution))
|
||||
print("Cluster", self.cluster_index, ": iteration", interration, "with current total distance", total_distance(current_solution))
|
||||
return best_solution
|
||||
Loading…
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Reference in New Issue
Block a user