class SA:
def __init__(self, model="Schaffer"):
if model == "Schaffer":
self.model = Schaffer()
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):
print 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))
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))
# 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"):
if model == "Schaffer":
self.model = Schaffer()
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):
print 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))
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))
# 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 MaxWalkSat:
def __init__(self, model = "Schaffer", probability = 0.5, no_steps = 10, baseline_top = -10**6, baseline_bottom = 10**6):
self.n = 6
self.p = probability
self.evals = 0
self.steps = no_steps
self.number_of_evaluations = 0
self.threshold = - 400
if model == "Osyczka":
self.model = Osyczka()
elif model == "Golinski":
self.model = Golinski()
elif model == "Kursawe":
self.model = Kursawe()
elif model == "Schaffer":
self.model = Schaffer()
self.model.resetBaselines()
self.current_state = self.model.get_random_state()
def modify_to_better_state(self, state, index):
if(index > len(self.model.top_bound)):
return None
increment = (self.model.top_bound[index] - self.model.bottom_bound[index])/self.steps
temp_state = list(state)
best_state = state
for _ in range(0, self.steps):
temp_state[index] += increment
self.evals += 1
if self.model.energy(temp_state) < self.model.energy(best_state) and self.model.are_constraints_satisfied(temp_state):
best_state = list(temp_state)
state = best_state
return state
def retry(self, state):
index = randint(0, (self.model.getNumberOfDecisions() - 1))
temp_state = list(state)
if self.p < random():
#print self.model.bottom_bound[index]
#print self.model.top_bound[index]
temp_state[index] = self.model.bottom_bound[index] + (self.model.top_bound[index] - self.model.bottom_bound[index])*random()
if self.model.are_constraints_satisfied(temp_state):
return temp_state
else:
return state
else:
temp_state = self.modify_to_better_state(temp_state, index)
if temp_state == state:
return temp_state
else:
return temp_state
def run(self):
max_tries = 100
max_changes = 50
self.model.resetBaselines()
best_state = self.model.get_random_state()
output = str()
lives = 5
ERA_LENGTH = 25
current_era = []
eras = []
for _ in range(0, max_tries):
current_state = self.model.get_random_state()
for i in range(0, max_changes):
if self.model.energy(current_state) < self.threshold:
return current_state
else:
new_state = self.retry(current_state)
operation = ""
if self.model.energy(new_state) > self.model.energy(best_state):
best_state = new_state
operation = "!"
elif self.model.energy(new_state) > self.model.energy(current_state):
operation = "+"
elif self.model.energy(new_state) < self.model.energy(current_state):
operation = "."
output += operation
current_era.append(self.model.normalize_energy(self.model.energy(current_state), 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 "reducing the life by 1 lives: " + str(lives)
else:
lives = 5
if lives <= 0:
print "Early termination"
return
if len(current_era) == ERA_LENGTH:
eras.append(current_era)
current_era = []
if len(output) == 50:
print "Lives: " + str(lives) + " " + output + " current best state energy(normalized) = " + str(self.model.energy(best_state)) + " Evaluations: " + str(self.evals)
output = ""
print output + " current best state energy(normalized) = " + str(self.model.aggregate_energy(best_state)) + " Evaluations: " + str(self.evals)
return best_state
