LouisD/a3-algorithmique-avancee
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

new stats

Browse Source
This commit is contained in:
Louis 2023-06-19 11:45:41 +02:00
parent c939724fe2
commit 312b168ed4
3 changed files with 45 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

BIN
.assets/images/stats_aco_alpha1-2_beta2-5.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 48 KiB

8
tests/101_analyse_aco.py
View File

@ -1,9 +1,11 @@
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from libs.aco import AntColony, total_distance from libs.aco import AntColony, total_distance
name = "att48" name = "berlin52"
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 = [[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],
optimal = 33523 [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
n_ants = 10 n_ants = 10
alpha = 1 alpha = 1

47
tests/103_analyse_aco_alpha_beta.py
View File

@ -2,16 +2,18 @@ import numpy as np
from matplotlib import pyplot as plt from matplotlib import pyplot as plt
from libs.aco import AntColony, total_distance from libs.aco import AntColony, total_distance
name = "att48" name = "berlin52"
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 = [[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],
optimal = 33523 [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
n_ants = 10 n_ants = 10
alpha = [1, 2] alpha = [1, 2]
beta = [1, 2] beta = [2, 3, 4, 5]
evaporation = 0.5 evaporation = 0.5
intensification = 2 intensification = 2
max_times = [0.5, 1, 1, 1, 1, 2] max_times = [1]
iterations = 1 iterations = 1
bar_width = 0.15 bar_width = 0.15
@ -21,6 +23,31 @@ x = np.arange(len(max_times))
fig, ax = plt.subplots() fig, ax = plt.subplots()
# Preparing the colormap
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
]
color_dict = {}
for i in range(len(alpha)): for i in range(len(alpha)):
for j in range(len(beta)): for j in range(len(beta)):
best_distances = [] best_distances = []
@ -30,12 +57,18 @@ for i in range(len(alpha)):
print("Running iteration number {}/{} ({} sec)".format(iteration + 1, iterations, max_time)) print("Running iteration number {}/{} ({} sec)".format(iteration + 1, iterations, max_time))
best_route = ant_colony.run() best_route = ant_colony.run()
best_distances.append(total_distance(best_route)) 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]))
# Defining a unique key for each alpha-beta pair
key = f"alpha={alpha[i]}_beta={beta[j]}"
if key not in color_dict:
color_dict[key] = colors[i * len(beta) + j]
ax.bar(x + (i * len(beta) + j) * bar_width, best_distances, bar_width, alpha=opacity, color=color_dict[key], label='alpha={} beta={}'.format(alpha[i], beta[j]))
ax.set_xlabel('Max Time') ax.set_xlabel('Max Time')
ax.set_ylabel('Best Distance') ax.set_ylabel('Best Distance')
ax.set_title('Best distances per max time for each alpha and beta') ax.set_title('Best distances per max time for each alpha and beta')
ax.set_xticks(x + bar_width / 2) ax.set_xticks(x + bar_width / 3)
ax.set_xticklabels(max_times) ax.set_xticklabels(max_times)
ax.legend() ax.legend()