-
Notifications
You must be signed in to change notification settings - Fork 0
/
SimulatedAnnealing.py
122 lines (96 loc) · 3.31 KB
/
SimulatedAnnealing.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
116
117
118
119
120
121
122
import numpy as np
import random
import decimal
import time
import math
def float_range(start, stop, step):
while start <= stop:
yield float(start)
start += decimal.Decimal(step)
values_rose = list(float_range(-5, 5, '0.001'))
values_himme = list(float_range(-4, 4, '0.001'))
def rosenbrock_fun(x):
"""Rosenbrock function
Domain: -5 < xi < 5
Global minimun: f_min(1,..,1)=0
a, b = 1, 100
f(x,y) = (a-x)^2 + b(y-x^2)^2
"""
return (1 - x[0])**2 + 100*(x[1] - x[0]**2)**2
def himmelblau_fun(x):
"""Himmelblau function
Domain: -4 < xi < 4
Global minimun: f_min(1,..,1)=0
f(x,y) = (x^2 + y - 11)^2 + (x - y^2 - 7)^2
"""
return (x[0]**2 + x[1] + 11)**2 + (x[0]+ x[1]**2 -7)**2
def get_neighbors_rose():
respuesta = []
for a in range(5):
respuesta.append([random.choice(values_rose),random.choice(values_rose)])
return respuesta
def get_cost_rose(state):
x = float('%.3f'%(-5.0+(float(state[0])*0.001)))
y = float('%.3f'%(-5.0+((float(state[1])*0.001))))
return rosenbrock_fun([x,y])
def simulated_annealing_rose(initial_state):
initial_temp = 90
final_temp = .1
alpha = 0.01
current_temp = initial_temp
current_state = initial_state
solution = current_state
while current_temp > final_temp:
neighbor = random.choice(get_neighbors_rose())
cost_diff = get_cost_rose(current_state) - get_cost_rose(neighbor)
if cost_diff > 0:
solution = neighbor
else:
if random.uniform(0, 1) < math.exp(-cost_diff / current_temp):
solution = neighbor
current_temp -= alpha
return solution
def get_neighbors_himme():
respuesta = []
for a in range(5):
respuesta.append([random.choice(values_himme),random.choice(values_himme)])
return respuesta
def get_cost_himme(state):
x = float('%.3f'%(-4.0+(float(state[0])*0.001)))
y = float('%.3f'%(-4.0+((float(state[1])*0.001))))
return himmelblau_fun([x,y])
def simulated_annealing_himme(initial_state):
initial_temp = 90
final_temp = .1
alpha = 0.01
current_temp = initial_temp
current_state = initial_state
solution = current_state
while current_temp > final_temp:
neighbor = random.choice(get_neighbors_himme())
cost_diff = get_cost_himme(current_state) - get_cost_himme(neighbor)
if cost_diff > 0:
solution = neighbor
else:
if random.uniform(0, 1) > math.exp(-cost_diff / current_temp):
solution = neighbor
current_temp -= alpha
return solution
vals = []
print("""
1) Función de rosenbrock
2) Funcion de Himmelblau
""")
op = int(input("\nIngrese su opción: "))
if op == 1:
for i in range(50):
StartTime = time.time()
vals.append(simulated_annealing_rose(['-5.0','-5.0']))
print(i+1,')', 'Position: ',vals[i], 'Best: ',rosenbrock_fun(vals[i]))
if op == 2:
for i in range(50):
StartTime = time.time()
vals.append(simulated_annealing_himme(['-4.0','-4.0']))
print(i+1,')', 'Position: ',vals[i], 'Best: ',himmelblau_fun(vals[i]))
EndTime = time.time()
print("Tiempo de ejecución: ", EndTime - StartTime)