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AC.py
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AC.py
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# 2OPT Implementation provided by Smail KOURTA (started from code by Rochak Gupta [rochakgupta], and tweaked it by adding numpy and using matrices), refactored into a command line program by Smail KOURTA
import numpy as np
import random
import argparse
import Parser
import time
class AC:
class Ant:
def __init__(self, alpha, beta, weights, pherom):
self.alpha = alpha
self.beta = beta
self.num_nodes = weights.shape[0]
self.weights = weights
self.pheroms = pherom
self.tour = None
self.distance = 0.0
def _select_node(self):
unvisited_nodes = [node for node in range(
self.num_nodes) if node not in self.tour]
rowWeights = self.weights[self.tour[-1]][unvisited_nodes]
rowPheroms = self.pheroms[self.tour[-1]][unvisited_nodes]
tauIetaI = [(rowPheroms[i] ** self.alpha) * ((1 / rowWeights[i]) ** self.beta) for i in
range(len(unvisited_nodes))]
roulette_wheel = np.sum(tauIetaI)
random_value = random.random()
for i, unvisited_node in enumerate(unvisited_nodes):
random_value -= tauIetaI[i] / roulette_wheel
if random_value <= 0:
return unvisited_node
def find_tour(self):
self.tour = [random.randint(0, self.num_nodes - 1)]
while len(self.tour) < self.num_nodes:
self.tour.append(self._select_node())
return self.tour
def get_distance(self):
self.distance = 0.0
for i in range(self.num_nodes):
self.distance += self.weights[self.tour[i]
][self.tour[(i + 1) % self.num_nodes]]
return self.distance
def __init__(self, weights, mode='ACS', colony_size=10, elitist_weight=1.0, min_scaling_factor=0.001, alpha=1.0,
beta=3.0,
rho=0.1, pheromone_deposit_weight=1.0, initial_pheromone=1.0, steps=100, nodes=None, labels=None):
self.mode = mode
self.colony_size = colony_size
self.elitist_weight = elitist_weight
self.min_scaling_factor = min_scaling_factor
self.rho = rho
self.pheromone_deposit_weight = pheromone_deposit_weight
self.steps = steps
self.num_nodes = weights.shape[0]
self.nodes = nodes
self.weights = weights
self.pheroms = np.full(
(weights.shape[0], weights.shape[1]), initial_pheromone)
if labels is not None:
self.labels = labels
else:
self.labels = range(1, self.num_nodes + 1)
self.ants = [self.Ant(alpha, beta, self.weights, self.pheroms)
for _ in range(self.colony_size)]
self.global_best_tour = None
self.global_best_distance = float("inf")
def _add_pheromone(self, tour, distance, weight=1.0):
pheromone_to_add = self.pheromone_deposit_weight / distance
for i in range(self.num_nodes):
self.pheroms[tour[i]][tour[(
i + 1) % self.num_nodes]] += weight * pheromone_to_add
def _acs(self):
for step in range(self.steps):
for ant in self.ants:
self._add_pheromone(ant.find_tour(), ant.get_distance())
if ant.distance < self.global_best_distance:
self.global_best_tour = ant.tour
self.global_best_distance = ant.distance
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.pheroms[i][j] *= (1.0 - self.rho)
def _elitist(self):
for step in range(self.steps):
for ant in self.ants:
self._add_pheromone(ant.find_tour(), ant.get_distance())
if ant.distance < self.global_best_distance:
self.global_best_tour = ant.tour
self.global_best_distance = ant.distance
self._add_pheromone(
self.global_best_tour, self.global_best_distance, weight=self.elitist_weight)
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.pheroms[i][j] *= (1.0 - self.rho)
def _max_min(self):
for step in range(self.steps):
iteration_best_tour = None
iteration_best_distance = float("inf")
for ant in self.ants:
ant.find_tour()
if ant.get_distance() < iteration_best_distance:
iteration_best_tour = ant.tour
iteration_best_distance = ant.distance
if float(step + 1) / float(self.steps) <= 0.75:
self._add_pheromone(iteration_best_tour,
iteration_best_distance)
max_pheromone = self.pheromone_deposit_weight / iteration_best_distance
else:
if iteration_best_distance < self.global_best_distance:
self.global_best_tour = iteration_best_tour
self.global_best_distance = iteration_best_distance
self._add_pheromone(self.global_best_tour,
self.global_best_distance)
max_pheromone = self.pheromone_deposit_weight / self.global_best_distance
min_pheromone = max_pheromone * self.min_scaling_factor
for i in range(self.num_nodes):
for j in range(i + 1, self.num_nodes):
self.pheroms[i][j] *= (1.0 - self.rho)
if self.pheroms[i][j] > max_pheromone:
self.pheroms[i][j] = max_pheromone
elif self.pheroms[i][j] < min_pheromone:
self.pheroms[i][j] = min_pheromone
def run(self):
# print('Started : {0}'.format(self.mode))
if self.mode == 'ACS':
start_time = time.time()
self._acs()
elif self.mode == 'Elitist':
start_time = time.time()
self._elitist()
else:
start_time = time.time()
self._max_min()
end_time = time.time()
# print('Ended : {0}'.format(self.mode))
print(self.global_best_tour)
print(self.global_best_distance)
print(end_time - start_time)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--instance",
help="Path to the instance file",
required=True,)
parser.add_argument("--mode",
help="AC Mode",
default="ACS")
parser.add_argument("--colony_size",
help="AC Colony Size",
default=10)
parser.add_argument("--elitist_weight",
help="AC Elitist Weight",
default=1.0)
parser.add_argument("--min_scaling_factor",
help="AC Min Scaling Factor",
default=0.001)
parser.add_argument("--alpha",
help="AC Alpha Parameter",
default=1.0)
parser.add_argument("--beta",
help="AC Beta Parameter",
default=3.0)
parser.add_argument("--rho",
help="AC Rho Parameter",
default=0.1)
parser.add_argument("--pheromone_deposit_weight",
help="AC Pheromone Deposit Weight",
default=1.0)
parser.add_argument("--initial_pheromone",
help="AC Initial Pheromone",
default=1.0)
parser.add_argument("--steps",
help="AC Steps",
default=100)
args = parser.parse_args()
instance = Parser.TSPInstance(args.instance)
instance.readData()
ac = AC(np.array(instance.data), mode=args.mode, colony_size=int(args.colony_size), elitist_weight=float(args.elitist_weight), min_scaling_factor=float(args.min_scaling_factor), alpha=float(args.alpha),
beta=float(args.beta), rho=float(args.rho), pheromone_deposit_weight=float(args.pheromone_deposit_weight), initial_pheromone=float(args.initial_pheromone), steps=int(args.steps), nodes=None, labels=None)
ac.run()