def value(self, x):
return math.fabs(x * math.sin(x))
if __name__ == '__main__':
# Formulate a problem with a 2D hill function and a single maximum value.
maximum = 30
initial = randrange(0, maximum)
p = AbsVariant(initial, maximum, delta=1.0)
print('Initial x: ' + str(p.initial) + '\t\tvalue: ' +
str(p.value(initial)))
# Solve the problem using hill-climbing.
startTime = time.time()
hill_solution = hill_climbing(p)
endTime = time.time() - startTime
print('Hill-climbing solution x: ' + str(hill_solution) +
'\tvalue: ' + str(p.value(hill_solution)) + '\t\ttime: ' +
str(endTime))
# Solve the problem using simulated annealing.
startTime = time.time()
annealing_solution = simulated_annealing(
p, exp_schedule(k=20, lam=0.005, limit=1000))
endTime = time.time() - startTime
print('Simulated annealing solution x: ' + str(annealing_solution) +
'\tvalue: ' + str(p.value(annealing_solution)) + '\t\ttime: ' +
str(endTime))
# ae '(0, 1): 10' is point 0's link to point 1 with a distance of 10
for i in range(0, mapSize):
for k in range(i + 1, mapSize):
path = (i, k)
path_dist = randrange(1, 11)
pathsList[path] = path_dist
# don't over-clutter the screen with paths and distances
if mapSize <= 10:
print('Paths:\t' + str(pathsList ))
problem = TSP(mapSize, pathsList)
t = time.time()
hill_climbing = hill_climbing(problem)
hc_time = time.time() - t
print('Hill climbing:\t' + str(hill_climbing)
+ '\n\tvalue: ' + str(problem.value(hill_climbing))
+ '\n\ttime: ' + str(hc_time)
)
t = time.time()
sim_annealing = simulated_annealing(problem, exp_schedule(k=20, lam=0.005, limit=1000))
sa_time = time.time() - t
print('Simulated annealing\t: ' + str(sim_annealing )
+ '\n\tvalue: ' + str(problem.value(sim_annealing ))
+ '\n\ttime: ' + str(sa_time)
)
t = time.time()
# Solve the problem using hill-climbing.
hill_solution = hill_climbing(p)
print('Hill-climbing solution x: ' + str(hill_solution) +
'\tvalue: ' + str(p.value(hill_solution)))
tDelta = time.time() - t
print("time to solve:\t" + str(tDelta))
#HCt.append((initial,tDelta))
HCt.append(
(initial, p.value(hill_solution))) #I know in random-restart
#you arn't supposed to store the solutions in memory, but I want to keep
#them for sake of graphing
# Solve the problem using simulated annealing.
t = time.time()
annealing_solution = simulated_annealing(
p, exp_schedule(k=20, lam=0.005, limit=maximum))
print('Simulated annealing solution x: ' + str(annealing_solution) +
'\tvalue: ' + str(p.value(annealing_solution)))
tDelta = time.time() - t
SAt.append((initial, p.value(annealing_solution)))
print("time to solve:\t" + str(tDelta))
x1, y1 = zip(*HCt)
x2, y2 = zip(*SAt)
#plt.scatter(*(x,y))
print("Best hill-climbing solution:\t" + str(max(y1)))
print("Best Simulated-annealing solution:\t" + str(max(y2)))
print("Average hill-climbing solution:\t" + str(sum(y1) / len(y1)))
print("Average simulated annealing solution:\t" + str(sum(y2) / len(y2)))
# plt.hist(y1)
# plt.show()
p = SineVariant(initial, maximum, delta=1.0)
print('Initial x: ' + str(p.initial) + '\t\tvalue: ' +
str(p.value(initial)))
# Solve the problem using hill-climbing.
t0 = time.time()
hill_solution = 0
for i in range(1, 10):
initial = randrange(0, maximum)
p = SineVariant(initial, maximum, delta=1.0)
hill_solution = max(hill_climbing(p), hill_solution)
t1 = time.time()
print('Hill-climbing solution x: ' + str(hill_solution) +
'\tvalue: ' + str(p.value(hill_solution)) + ' \ttime: ' +
str(t1 - t0))
# Solve the problem using simulated annealing.
t2 = time.time()
annealing_solution = 0
for i in range(1, 10):
initial = randrange(0, maximum)
p = SineVariant(initial, maximum, delta=1.0)
annealing_solution = max(
simulated_annealing(p, exp_schedule(k=20, lam=0.005, limit=1000)),
annealing_solution)
t3 = time.time()
print('Simulated annealing solution x: ' + str(annealing_solution) +
'\tvalue: ' + str(p.value(annealing_solution)) + '\ttime: ' +
str(t3 - t2))
if distance not in TSPstate:
TSPaction.append(distance)
return TSPaction
if __name__ == '__main__':
#change the number of cities it travels
randomCity = 50
randomPath = {}
#indicates path and distances that have been randomized
for cityNumber in range(0, randomCity):
for space in range(cityNumber + 1, randomCity):
local = (cityNumber, space)
newPathDistance = randrange(1, 9)
randomPath[local] = newPathDistance
TSPproblem = TSP(randomCity, randomPath)
#implementations
hillClimbingImp = hill_climbing(TSPproblem)
simulatedAnnealingImp = simulated_annealing(TSPproblem, exp_schedule(k=10, lam=0.005, limit=100))
#prints hill climbing and simulated annealing results
print('Hill climbing:\t' + str(hillClimbingImp)
+ '\n\tvalue: ' + str(TSPproblem.value(hillClimbingImp))
)
print('Simulated annealing\t: ' + str(simulatedAnnealingImp)
+ '\n\tvalue: ' + str(TSPproblem.value(simulatedAnnealingImp))
)
