class SA:
def __init__(self, model = "Schaffer"):
self.evals = 0
if model == "Schaffer":
self.model = Schaffer()
elif model == "Osyczka":
self.model = Osyczka()
elif model == "Golinski":
self.model = Golinski()
elif model == "Kursawe":
self.model = Kursawe()
def __repr__(self):
return time.strftime("%Y-%m-%d %H:%M:%S") + "\nSimulated Annealing on the Schaffer model\n"
def P(self, old_e, new_e, k):
return math.e ** ((old_e - new_e) / k)
def run(self):
kmax = 1000
max_e = -0.1
output = ''
current_s = self.model.get_random_state()
best_s = current_s
current_e = self.model.normalize_energy(self.model.energy(current_s), self.model.baseline_low, self.model.baseline_high)
best_e = current_e
k = 1
while k < kmax and current_e > max_e:
neighbor_s = self.model.get_random_state()
# print 'Neighbor: ' + str(neighbor_s)
neighbor_e = self.model.normalize_energy(self.model.energy(neighbor_s), self.model.baseline_low, self.model.baseline_high)
# print current_e, neighbor_e, tmp
if neighbor_e < best_e:
best_s, best_e = neighbor_s, neighbor_e
output += ' !'
if neighbor_e < current_e:
current_s, current_e = neighbor_s, neighbor_e
output += ' +'
elif self.P(current_e, neighbor_e, (1-(float(k)/kmax))**5) > random.random():
current_s, current_e = neighbor_s, neighbor_e
output += ' ?'
else:
output += ' .'
if k % 25 == 0:
print ', %4d, : %d, %25s' % (k, self.model.denormalize_energy(best_e), output)
output = ''
k += 1
print
print 'Best State Found: ' + str(best_s)
print 'Energy At Best State: ' + str(self.model.denormalize_energy(best_e))
print
return best_e
class SA:
def __init__(self, model = "Schaffer"):
self.evals = 0
if model == "Schaffer":
self.model = Schaffer()
elif model == "Osyczka":
self.model = Osyczka()
elif model == "Golinski":
self.model = Golinski()
elif model == "Kursawe":
self.model = Kursawe()
def __repr__(self):
return time.strftime("%Y-%m-%d %H:%M:%S") + "\nSimulated Annealing on the Schaffer model\n"
def P(self, old_e, new_e, k):
return math.e ** ((old_e - new_e) / k)
def run(self):
kmax = 1000
max_e = -0.1
output = ''
current_s = self.model.get_random_state()
best_s = current_s
current_e = self.model.normalize_energy(self.model.energy(current_s), self.model.baseline_low, self.model.baseline_high)
best_e = current_e
k = 1
current_era = []
eras = []
lives = 5
ERA_LENGTH = 10
while k < kmax and current_e > max_e:
neighbor_s = self.model.get_random_state()
# print 'Neighbor: ' + str(neighbor_s)
neighbor_e = self.model.normalize_energy(self.model.energy(neighbor_s), self.model.baseline_low, self.model.baseline_high)
# print current_e, neighbor_e, tmp
if neighbor_e < best_e:
best_s, best_e = neighbor_s, neighbor_e
output += ' !'
if neighbor_e < current_e:
current_s, current_e = neighbor_s, neighbor_e
output += ' +'
elif self.P(current_e, neighbor_e, (1-(float(k)/kmax))**5) > random.random():
current_s, current_e = neighbor_s, neighbor_e
output += ' ?'
else:
output += ' .'
current_era.append(self.model.normalize_energy(current_e, self.model.baseline_low, self.model.baseline_high))
if len(current_era) == ERA_LENGTH and len(eras) > 0:
if a12(current_era, eras[-1]) < 0.56:
lives -= 1
print "Lives:" + str(lives) + " a12 statistic:" + str(a12(current_era, eras[-1])),
else:
lives = 5
if len(current_era) == ERA_LENGTH:
eras.append(current_era)
current_era = []
if lives <= 0:
print "Early termination"
break
if k % 25 == 0:
print ', %4d, : %d, %25s' % (k, self.model.denormalize_energy(best_e), output)
output = ''
k += 1
print
print 'Best State Found: ' + str(best_s)
print 'Energy At Best State: ' + str(self.model.denormalize_energy(best_e))
print
return best_e
class SA:
def __init__(self, model="Schaffer"):
self.evals = 0
if model == "Schaffer":
self.model = Schaffer()
elif model == "Osyczka":
self.model = Osyczka()
elif model == "Golinski":
self.model = Golinski()
elif model == "Kursawe":
self.model = Kursawe()
def __repr__(self):
return time.strftime(
"%Y-%m-%d %H:%M:%S"
) + "\nSimulated Annealing on the Schaffer model\n"
def P(self, old_e, new_e, k):
return math.e**((old_e - new_e) / k)
def run(self):
kmax = 1000
max_e = -0.1
output = ''
current_s = self.model.get_random_state()
best_s = current_s
current_e = self.model.normalize_energy(self.model.energy(current_s),
self.model.baseline_low,
self.model.baseline_high)
best_e = current_e
k = 1
current_era = []
eras = []
lives = 5
ERA_LENGTH = 10
while k < kmax and current_e > max_e:
neighbor_s = self.model.get_random_state()
# print 'Neighbor: ' + str(neighbor_s)
neighbor_e = self.model.normalize_energy(
self.model.energy(neighbor_s), self.model.baseline_low,
self.model.baseline_high)
# print current_e, neighbor_e, tmp
if neighbor_e < best_e:
best_s, best_e = neighbor_s, neighbor_e
output += ' !'
if neighbor_e < current_e:
current_s, current_e = neighbor_s, neighbor_e
output += ' +'
elif self.P(current_e, neighbor_e,
(1 - (float(k) / kmax))**5) > random.random():
current_s, current_e = neighbor_s, neighbor_e
output += ' ?'
else:
output += ' .'
current_era.append(
self.model.normalize_energy(current_e, self.model.baseline_low,
self.model.baseline_high))
if len(current_era) == ERA_LENGTH and len(eras) > 0:
if a12(current_era, eras[-1]) < 0.56:
lives -= 1
print "Lives:" + str(lives) + " a12 statistic:" + str(
a12(current_era, eras[-1])),
else:
lives = 5
if len(current_era) == ERA_LENGTH:
eras.append(current_era)
current_era = []
if lives <= 0:
print "Early termination"
break
if k % 25 == 0:
print ', %4d, : %d, %25s' % (
k, self.model.denormalize_energy(best_e), output)
output = ''
k += 1
print
print 'Best State Found: ' + str(best_s)
print 'Energy At Best State: ' + str(
self.model.denormalize_energy(best_e))
print
return best_e
