def run(self):
'''
Function to run the rank based Ant Colony Optimization algorithm
'''
best_ant = Ant(0, 0, [])
for i in range(self._n_iterations):
colony = []
for j in range(self._n_ants):
ant = Ant(0, self._distances.shape[0],
copy.deepcopy(self._vehicles))
for x in range(len(ant.vehicles)):
ant.vehicles[x].put_node_path(0, self._occupancies,
self._distances, self._times)
ant.build_path(self._distances, self._times, self._occupancies,
self._pheromone, self._alpha, self._beta)
ant.calculate_distance(self._distances)
ant.objectve_function(self._distance_cost)
colony.append(ant)
n_bests_ants = self.find_n_bests_ants(colony)
if n_bests_ants[0].of_value < best_ant.of_value:
best_ant = n_bests_ants[0]
self.update_pheromone(best_ant, n_bests_ants)
#print("Iteration: %d Best OF: %f" %(i + 1, best_ant.of_value))
return best_ant
def run(self):
'''
Function to run the rank based Ant Colony Optimization algorithm
'''
best_ant = Ant(0, 0)
for i in range(self.n_iterations):
print("Iteration: %d Best OF: %f" % (i, best_ant.of()))
colony = []
for j in range(self.n_ants):
ant = Ant(np.random.randint(self.distances.shape[0]),
self.distances.shape[0])
ant.build_path(self.distances, self.pheromone, self.alpha,
self.beta)
ant.calculate_distance(self.distances)
ant.objectve_function(self.distance_cost)
colony.append(ant)
n_bests_ants = self.find_n_bests_ants(colony)
if n_bests_ants[0].of() < best_ant.of():
best_ant = n_bests_ants[0]
self.update_pheromone(best_ant, n_bests_ants)
print(self.pheromone)
return best_ant