def _generate_random_chromosome_(self):
route = self.graph.nodes()
shuffle(route)
solution = TSPSolver.nodes_to_solution(self, route)
TSPSolver.set_solution(self, solution[0], solution[1])
distance = self.graph.route_distance(solution[1])
return solution[0][:-1], distance
def _complete_generation_(self, population, new_population):
elite = sorted(population, key=lambda tup: tup[1])[0]
solution = TSPSolver.nodes_to_solution(self, elite[0])
TSPSolver.set_solution(self, solution[0], solution[1])
s_population = sorted(new_population, key=lambda tup: tup[1])[::-1]
new_population[new_population.index(s_population[0])] = elite
return new_population
def _initial_population_from_acs_(self):
self._acs_.solve()
population = []
for ant in self._acs_.ants.values():
route = ant.route[:-1]
solution = TSPSolver.nodes_to_solution(self, route)
TSPSolver.set_solution(self, solution[0], solution[1])
distance = self.graph.route_distance(solution[1])
population.append((solution[0][:-1], distance))
while len(population) < self._chromosomes_:
population.append(self._generate_random_chromosome_())
return population
def solve(self, show_progress=False):
self.show_progress = show_progress
TSPSolver.start_solving(self)
last_solution = self._new_solution_()
while not self._conditions_met_():
TSPSolver.update_progress(
self, "> Temp. = {0}, last solution - {1}".format(
round(self.t, 3), last_solution))
new_solution = self._new_solution_(last_solution)
TSPSolver.set_solution(self, new_solution[0], new_solution[1])
if self._accept_(last_solution, new_solution) is True:
last_solution = new_solution
self.t = self.t * self.decrement
TSPSolver.end_solving(self)
def _initial_population_using_ants(self, num_ants, beta):
population = []
ants = {}
for a in xrange(0, num_ants):
ants[a] = Ant(self.graph, self.graph.nodes())
for _ in xrange(num_ants, self._chromosomes):
population.append(self._generate_random_chromosome())
for ant in ants.itervalues():
ant.start()
while len(ant.remaining_cities()) > 0:
self._initial_population_try_to_visit_city(ant)
route = ant.route[:]
solution = TSPSolver.nodes_to_solution(self, route)
TSPSolver.set_solution(self, solution[0], solution[1])
distance = self.graph.route_distance(solution[1])
population.append((solution[0][:-1], distance))
return population
def solve(self, show_progress=False):
self.show_progress = show_progress
TSPSolver.start_solving(self)
all_nodes = range(0, len(self.graph.nodes()))
combinations = permutations(all_nodes)
for combination in combinations:
TSPSolver.update_progress(self,
"Combination - %s" % (combination, ))
if TSPSolver.conditions_met(self):
self.solution_canceled = True
break
last_node = combination[0]
full_route = [last_node]
for i in xrange(1, len(combination)):
next_node = combination[i]
full_route += shortest_path(self.graph, last_node,
next_node)[1:]
last_node = next_node
route = list(combination)
route.append(combination[0])
full_route += shortest_path(self.graph, last_node,
combination[0])[1:]
TSPSolver.set_solution(self, route, full_route)
TSPSolver.end_solving(self)
def _route_to_chromosome_(self, route):
solution = TSPSolver.nodes_to_solution(self, route)
TSPSolver.set_solution(self, solution[0], solution[1])
return solution[0][:-1], self.graph.route_distance(solution[1])