def tsp():
ants_num = int(ants_entry.get())
iter_num = int(iter_entry.get())
import paco
import matplotlib.pyplot as plt
num_cities = 48
# Intialize the ACO algorithm with some parameter values
aco = paco.ACO(num_cities,
initial_pheromone=1,
alpha=1,
beta=3,
pheromone_deposit=2,
evaporation_constant=0.6)
with open("./att48.txt") as f:
content = f.readlines()
content = [x.strip() for x in content]
for line in content:
items = line.split()
num = int(items[0]) - 1
x_cord = int(items[1])
y_cord = int(items[2])
aco.add_cities(paco.City(num, x_cord, y_cord))
# run the aco algorithm and return the shortest path
shortest_path = aco.get_best_path(num_ants=ants_num, num_steps=iter_num)
# print the shortest path length found in the aco
print("Number of ants used: {}".format(ants_num))
print("Shortest route found: {0:.3f}".format(aco.shortest_path_len))
#plt.axes([0.15, 0.15, 0.8, 0.8])
plt.figure(1, figsize=[8, 6], facecolor='#F0F0F0')
plt.margins(0.1, 0.1)
for i, c in enumerate(aco.cities): # output the cities to a plot
if i == 0:
plt.title("Visualization of ACO algorithms on TSP (48 cities)")
plt.ylabel("Y - Coordinates of the cities")
plt.xlabel("X - Coordinates of the cities")
plt.plot(c.x, c.y,
'gx') # the first city to be printed will be green
else:
plt.plot(c.x, c.y, 'ro')
for i in range(
0,
len(shortest_path) - 1
): # plot connecting lines between each city visited in the order they are visited
plt.plot([shortest_path[i].x, shortest_path[i + 1].x],
[shortest_path[i].y, shortest_path[i + 1].y],
'c-',
linewidth=2.0,
alpha=0.4)
plt.pause(0.05)
plt.show()
import paco
import random
import math
import matplotlib.pyplot as plt
num_cities = 48
# Intialize the ACO algorithm with some parameter values
aco = paco.ACO(num_cities,
initial_pheromone=1,
alpha=1,
beta=3,
pheromone_deposit=2,
evaporation_constant=0.6)
with open("./att48.txt") as f:
content = f.readlines()
content = [x.strip() for x in content]
for line in content:
items = line.split()
num = int(items[0]) - 1
x_cord = int(items[1])
y_cord = int(items[2])
aco.add_cities(paco.City(num, x_cord, y_cord))
# run the aco algorithm and return the shortest path
ants = 20
shortest_path = aco.get_best_path(num_ants=ants, num_steps=10)
# print the shortest path length found in the aco
print("Number of ants used: {}".format(ants))
print("Shortest route found: {0:.3f}".format(aco.shortest_path_len))
import paco
import random
import math
import matplotlib.pyplot as plt
num_cities = 100 # configure how many cities to populate the world with
world_x = 100 # set the max x,y dimensions for the world
world_y = 100
increment = 360.0 / num_cities # calculate the increment required if we want to lay the cities out in a circle
aco = paco.ACO(num_cities,
initial_pheromone=1,
alpha=1,
beta=3,
epsilon=0.1,
pheromone_deposit=2,
evaporation_constant=0.6
) # intialize the aco algorithm with our parameters
# see website for discussion of the above parameters
for i in range(0, num_cities):
#aco.add_cities(paco.City(i, random.uniform(0,world_x), random.uniform(0,world_y))) # use this line for randomly distributed cities
angle = i * increment # use this line and the next for cities distributed in a circle
aco.add_cities(
paco.City(i, math.sin(math.radians(angle)),
math.cos(math.radians(
angle)))) # populate the aco instance with the cities
shortest_path = aco.get_best_path(
num_ants=3,