class MaxWalkSat:
def __init__(self,
model="Kursawe",
probability=0.5,
no_steps=20,
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()
elif model == "DTLZ7":
self.model = DTLZ7(10, 2)
self.model.resetBaselines()
#self.threshold = self.model.baseline_low
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():
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 = 100
self.model.resetBaselines()
best_state = self.model.get_random_state()
output = str()
MAX_LIVES = 10
ERA_LENGTH = 10
era_List = []
current_era = []
lives = MAX_LIVES
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 = ""
while new_state == None:
new_state = self.retry(current_state)
if self.model.type1(best_state, new_state):
best_state = new_state
operation = "!"
elif self.model.type1(current_state, new_state):
operation = "+"
elif self.model.type1(new_state, current_state):
operation = "."
current_era.append(new_state)
if len(current_era) == ERA_LENGTH and len(era_List) > 0:
lives += self.model.type2(current_era, era_List[-1])
if lives <= 0:
print "Early termination"
break
era_List.append(current_era)
current_era = []
output += operation
if len(output) == 50:
output + " current best state energy = " + str(
best_state) + " Evaluations: " + str(self.evals)
output = ""
print output + " current best state energy = " + str(
best_state) + " Evaluations: " + str(self.evals)
return best_state
class MaxWalkSat:
def __init__(self, model = "Kursawe", probability = 0.5, no_steps = 20, 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()
elif model == "DTLZ7":
self.model = DTLZ7(10, 2)
self.model.resetBaselines()
#self.threshold = self.model.baseline_low
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():
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 = 100
self.model.resetBaselines()
best_state = self.model.get_random_state()
output = str()
MAX_LIVES = 10
ERA_LENGTH = 10
era_List = []
current_era = []
lives = MAX_LIVES
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 = ""
while new_state == None:
new_state = self.retry(current_state)
if self.model.type1(best_state, new_state):
best_state = new_state
operation = "!"
elif self.model.type1(current_state, new_state):
operation = "+"
elif self.model.type1(new_state, current_state):
operation = "."
current_era.append(new_state)
if len(current_era) == ERA_LENGTH and len(era_List) > 0:
lives += self.model.type2(current_era, era_List[-1])
if lives <= 0:
print "Early termination"
break
era_List.append(current_era)
current_era = []
output += operation
if len(output) == 50:
output + " current best state energy = " + str(best_state) + " Evaluations: " + str(self.evals)
output = ""
print output + " current best state energy = " + str(best_state) + " Evaluations: " + str(self.evals)
return best_state
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
class MaxWalkSat:
def __init__(self, model = "Kursawe", 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
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.threshold = self.model.baseline_low
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()
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.aggregate_energy(new_state) < self.model.aggregate_energy(best_state):
best_state = new_state
operation = "!"
elif self.model.aggregate_energy(new_state) < self.model.aggregate_energy(current_state):
operation = "+"
elif self.model.aggregate_energy(new_state) > self.model.aggregate_energy(current_state):
operation = "."
output += operation
if len(output) == 50:
print output + " best_energy = " + str(self.model.energy(best_state)) + " Evaluations: " + str(self.evals)
output = ""
print output + " best_energy = " + str(self.model.energy(best_state)) + " Evaluations: " + str(self.evals)
return best_state
