def get_tour(self):
if self._tour:
return self._tour
tour = Tour(0, self.cost_map)
solution = np.argsort(np.argsort(self.position))
for i in solution:
tour.add(i)
self._tour = tour
return tour
class ParticleSwarmOptimization(Algorithm):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
total_cities = len(self.cities)
self.particles = [
Particle(total_cities, self.cost_map)
for _ in range(number_particles)
]
self.gbest_value = float('inf')
self.gbest_position = None
self.set_pbest()
self.set_gbest()
def set_pbest(self):
for particle in self.particles:
fitness_cadidate = particle.get_tour().cost
if fitness_cadidate < particle.pbest_value:
particle.pbest_value = fitness_cadidate
particle.pbest_position = deepcopy(particle.position)
def set_gbest(self):
for particle in self.particles:
best_fitness_cadidate = particle.get_tour().cost
if best_fitness_cadidate < self.gbest_value:
self.gbest_value = particle.get_tour().cost
self.gbest_position = deepcopy(particle.position)
def move_particles(self):
for particle in self.particles:
new_velocity = (W * particle.velocity)\
+ c1 * random.random() * (particle.pbest_position - particle.position)\
+ c2 * random.random() * (self.gbest_position - particle.position)
particle.velocity = new_velocity
particle.move()
def get_tour(self):
tour = Tour(0, self.cost_map)
solution = np.argsort(np.argsort(self.gbest_position))
for i in solution:
tour.add(i)
return tour
def iterate(self):
self.move_particles()
self.set_pbest()
self.set_gbest()
self.best = Tour(0, self.cost_map)
solution = np.argsort(np.argsort(self.gbest_position))
for i in solution:
self.best.add(i)
def get_tour(self):
tour = Tour(0, self.cost_map)
solution = np.argsort(np.argsort(self.gbest_position))
for i in solution:
tour.add(i)
return tour
