def _crossover_ox(self, parent1, parent2):
"""Performs order crossover to create a child path from two given
parent paths.
:param Path parent1: First parent path.
:param Path parent2: Second parent path.
:return: Child path.
:rtype: Path
"""
# Initial child path
child = Path(len(parent1))
# Copy random subpath from parent 1 to child
start, end = self._rand_subpath()
subpath = parent1.path[start:end + 1]
tmp = parent2.path
# Rotate tmp with pivot in the end + 1
tmp = tmp[end + 1:] + tmp[:end + 1]
# Remove cities found in subpath from parent 2
tmp = list(filter(lambda x: x not in subpath, tmp))
# Join subpath and tmp to form a child
child.path = subpath + tmp
# Rotate the path so it always starts at 0
last_zero_idx = len(child) - child[::-1].index(0) - 1
child.path = child[last_zero_idx:] + child[:last_zero_idx]
child.distance = self.tsp.path_dist(child)
return child
def test_set_path_exception(self):
data = [[1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 6, 7], [1], []]
path = Path(6)
for p in data:
with self.subTest(path=p):
with self.assertRaises(ValueError):
path.path = p
def test_set_path(self):
data = [[1, 2, 3, 4, 5, 6], [9, 20, 1, 5, 55, 7], [0, 0, 0, 0, 0, 0]]
path = Path(6)
for p in data:
with self.subTest(path=p):
path.path = p
self.assertListEqual(path._path, p)
def _init_population(self):
"""Initializes population by creating specified number of random paths.
"""
self._population.clear()
for _ in range(self.population_size):
path = Path(self.tsp.dimension + 1)
path.path = list(range(self.tsp.dimension)) + [0]
path.shuffle(1, -1)
path.distance = self.tsp.path_dist(path)
self._population.append(path)
self._population.sort(key=lambda p: p.distance)
def solve(self, tsp, steps=True):
# Make sure given argument is of correct type
if not isinstance(tsp, TSP):
raise TypeError('solve() argument has to be of type \'TSP\'')
self.tsp = tsp
# Create starting path: 0, 1, 2, ..., 0, this path will be permuted
path = Path(self.tsp.dimension + 1)
path.path = list(range(len(path) - 1)) + [0]
path.distance = self.tsp.path_dist(path)
# Best path
min_path = deepcopy(path)
# Create permutations skipping the last stop (return to 0)
perms = permutations(path.path[1:-1])
if steps:
total = factorial(self.tsp.dimension - 1)
# Start the timer
self._start_timer()
# Loop through all permutations to find the shortest path
for i, perm in enumerate(perms):
path.path = [0] + list(perm) + [0]
path.distance = self.tsp.path_dist(path)
if path.distance < min_path.distance:
min_path = deepcopy(path)
if steps:
# Need to use deepcopies because object could change before the
# reference will be used
yield SolverState(self._time(), i / total, deepcopy(path),
deepcopy(min_path))
yield SolverState(self._time(), 1, None, min_path, True)
def solve(self, tsp, steps=True):
# Make sure given argument is of correct type
if not isinstance(tsp, TSP):
raise TypeError('solve() argument has to be of type \'TSP\'')
self.tsp = tsp
# Total number of iterations or time for calculating progress
if steps:
current = 0
iters = log(self.end_temp / self.init_temp, 1 - self.cooling_rate)
total = self.run_time if self.run_time else iters
# Start with random path
cur_path = Path(self.tsp.dimension + 1)
cur_path.path = list(range(len(cur_path) - 1)) + [0]
cur_path.shuffle(1, -1)
cur_path.distance = self.tsp.path_dist(cur_path)
# And set it as current minimum
min_path = deepcopy(cur_path)
# Start the timer
self._start_timer()
# Init temperature
temp = self.init_temp
# Repeat as long as system temperature is higher than minimum
while True:
# Update iteration counter ro time counterif running in step mode
if steps:
current = self._time_ms() if self.run_time else current + 1
# Get random neighbour of current path
new_path = self._rand_neigh(cur_path)
# Difference between current and new path
delta_dist = new_path.distance - cur_path.distance
# If it's shorter or equal
if delta_dist <= 0:
# If it's shorter set it as current minimum
if new_path.distance < min_path.distance:
min_path = deepcopy(new_path)
# Set new path as current path
cur_path = deepcopy(new_path)
elif exp(-delta_dist / temp) > random():
# If path is longer accept it with random probability
cur_path = deepcopy(new_path)
# Cooling down
temp *= 1 - self.cooling_rate
# Terminate search after reaching end temperature
if not self.run_time and temp < self.end_temp:
break
# Terminate search after exceeding specified runtime
# We use `total` to not have to convert to nanoseconds every time
if self.run_time and self._time_ms() >= self.run_time:
break
# Report current solver state
if steps:
yield SolverState(self._time(), current / total,
deepcopy(new_path), deepcopy(min_path))
yield SolverState(self._time(), 1, None, deepcopy(min_path), True)
