-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathexample.py
115 lines (93 loc) · 4.47 KB
/
example.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
import time
import re
import numpy as np
from colonyModule.Node import Node
from colonyModule.Colony import Colony
def read_dataset(path):
dataset = open(path, "r")
i = 0
while True:
line = dataset.readline()
if not line:
break
if(i == 0):
n_nodes = int(line)
costs = {(i, j): 0 for i in range(n_nodes) for j in range(n_nodes)}
elif(i == 1):
demand = [int(q) for q in line.split()]
elif(i == 2):
capacity_vehicles = int(line)
else:
j = 0
for el in line.split():
costs[(i-3, j)] = float(el)
j = j+1
i = i+1
dataset.close()
return (n_nodes, costs, demand, capacity_vehicles)
def test_datasets(datasets, colony_size, alpha, beta, gamma, lam, rho, sigma, iterations):
solutions = []
for dataset in datasets:
n_nodes, cost_dict, demand, max_load = read_dataset(dataset["file"])
nodes = []
for i in range(n_nodes):
nodes.extend([Node(i)])
def cost_function(from_node, to_node):
return cost_dict[(from_node.id, to_node.id)]
cost_matrix = np.array(list(cost_dict.values())).reshape(n_nodes, n_nodes)
try:
colony = Colony(nodes, demand, nodes[0], max_load, colony_size, alpha, beta,
gamma, lam, rho, sigma, iterations, cost_function, None, cost_matrix)
start = time.time()
solution = colony.foraging()
end = time.time()
datasetName = re.sub(r"\/.*\/", "", dataset["file"])
solution["dataset"] = datasetName
solution["time"] = round(end-start, 2)
solution["gap"] = round(
(((solution["cost"]/dataset["best"])-1)*100), 2)
solution["route"] = [node.id for route in solution["route"]
for node in route]
solutions.append(solution)
print(datasetName + " ok")
except IndexError as e:
print(datasetName + " no", e)
return solutions
if __name__ == "__main__":
datasets = [{"file": "dataset/parma5.txt", "best": 19500},
{"file": "dataset/bergamo5.txt", "best": 7900},
{"file": "dataset/parma8.txt", "best": 22700},
{"file": "dataset/bergamo8.txt", "best": 10400},
{"file": "dataset/parma9.txt", "best": 23400},
{"file": "dataset/bergamo9.txt", "best": 10200},
{"file": "dataset/parma10.txt", "best": 25400},
{"file": "dataset/bergamo10.txt", "best": 10800}]
datasets += [{"file": "dataset/1Bari30.txt", "best": 14600},
{"file": "dataset/2Bari20.txt", "best": 15700},
{"file": "dataset/3Bari10.txt", "best": 20600},
{"file": "dataset/4ReggioEmilia30.txt", "best": 16900},
{"file": "dataset/5ReggioEmilia20.txt", "best": 23200},
{"file": "dataset/6ReggioEmilia10.txt", "best": 32500},
{"file": "dataset/7Bergamo30.txt", "best": 12600},
{"file": "dataset/8Bergamo20.txt", "best": 12700},
{"file": "dataset/9Bergamo12.txt", "best": 13500},
{"file": "dataset/10Parma30.txt", "best": 29000},
{"file": "dataset/11Parma20.txt", "best": 29000},
{"file": "dataset/12Parma10.txt", "best": 32500},
{"file": "dataset/13Treviso30.txt", "best": 29259},
{"file": "dataset/14Treviso20.txt", "best": 29259},
{"file": "dataset/15Treviso10.txt", "best": 31443},
{"file": "dataset/16LaSpezia30.txt", "best": 20746},
{"file": "dataset/17LaSpezia20.txt", "best": 20746},
{"file": "dataset/18LaSpezia10.txt", "best": 22811},
{"file": "dataset/19BuenosAires30.txt", "best": 76999},
{"file": "dataset/20BuenosAires20.txt", "best": 91619},
{"file": "dataset/21Ottawa30.txt", "best": 16202},
{"file": "dataset/22Ottawa20.txt", "best": 16202},
{"file": "dataset/23Ottawa10.txt", "best": 17576}]
solutions = test_datasets(datasets, colony_size=50, alpha=6,
beta=5, gamma=5, lam=5, rho=0.4, sigma=1, iterations=100)
print("\n")
for solution in solutions:
print(solution["dataset"], solution["cost"],
solution["time"], solution["gap"])