def main(args):
filename = args.file
num_ants = args.ants
iterations = args.iterations
pher = 0.5
a = args.alpha
b = args.beta
decay = args.decay
seed = args.seed
stats = args.stats
num_points, p_median, points = read_input(filename)
aco = AntColony(num_ants,
iterations,
pher,
a,
b,
decay,
seed=seed,
stat_file=stats)
aco.set_data(num_points, p_median, points)
solution = aco.run()
print('Best distance: ' + str(solution))
type=int)
parser.add_argument('iterations',
help='the number of iterations to be \
perfomed by the algorithm',
type=int)
parser.add_argument('map', help = 'the map to calculate the path from', \
type = str)
parser.add_argument('p', help = 'controls the amount of pheromone that is \
evaporated, range[0-1], precision 0.05' , \
type = float, choices = \
np.around(np.arange(0.0,1.05,0.05),decimals = 2))
parser.add_argument('Q', help = 'controls the amount of pheromone that is \
added' , \
type = float)
parser.add_argument('-d','--display', default = 0, action = 'count', \
help = 'display the map and the \
resulting path' )
args = parser.parse_args()
return args.ants, args.iterations, args.map, args.p, args.Q, args.display
if __name__ == '__main__':
ants, iterations, map_path, p, Q, display = arguments_parsing()
# Get the map
Map = Map(map_path)
Colony = AntColony(Map, ants, iterations, p, Q)
path = Colony.calculate_path()
print path
if display > 0:
Map.represent_path(path)
beta = 2 # Visibility factor
e = .5 # Evaporation rate
el = 2 # Amount of elite ants
# Matrices
am = get_adjacency_matrix(M)
dist_m = fill_dis_matr(M) # Distances matrix
pheromones = np.ones((M, M))
print_matrix(am, M, "Adjacency Matrix")
print_matrix(pheromones, M, "Pheromones Matrix")
plot_graph(am, dist_m)
full_search_result = full_search.find_best_way(dist_m)
ac = AntColony(distances=dist_m,
n_ants=M,
n_best=2,
n_iterations=100,
decay=0.5,
alpha=1,
beta=1)
aco_result = ac.run()
def main():
pass
if __name__ == '__main__':
main()
end_num = 90
for line in sys.stdin:
line = line.strip()
dic = line
dic = json.loads(dic) #we make dictionary from string
all_ants = float(dic['n_ants'])
distances = np.asarray(dic['distances'])
pheromones = np.asarray(dic['pheromones'])
upper_bond = int(math.ceil(all_ants/batch_size))
for splits_number in range(upper_bond):
dic2 = dic.copy()
if batch_size*(1+splits_number) < all_ants:
dic2['n_ants'] = batch_size
dic2['n_best'] = dic2['n_ants']
else:
dic2['n_ants'] = int(all_ants - splits_number*batch_size)
dic2['n_best'] = dic2['n_ants']
ant_colony = AntColony(distances, dic2['n_ants'], dic2['n_iterations']-1, dic2['decay'], dic2['alpha'],
dic2['beta'], pheromones)
shortest_path, new_pheromone = ant_colony.run(start_num, end_num)
dic2['distances'] = dic['distances']
dic2['pheromones'] = dic['pheromones']
print('%s\t%s' % (shortest_path, json.dumps(dic2)))
#!/usr/bin/env python
from aco import AntColony
import numpy as np
import json
import sys
start_num = 1
end_num = 90
input_copy_filename = "../data/input500_10_800.txt"
file_input_copy = open(input_copy_filename, "r")
for input_copy in file_input_copy:
dic = input_copy.strip()
dic = json.loads(dic)
dic['distances'] = np.asarray(dic['distances'])
ant_colony = AntColony(dic['distances'], dic['n_ants'], dic['n_iterations'],
dic['decay'], dic['alpha'], dic['beta'])
optimal_path = ant_colony.run(start_num, end_num)
print(optimal_path[1])
parser.add_argument("--asys", action="store_true")
parser.add_argument("--eas", action="store_true")
parser.add_argument("--ras", action="store_true")
parser.add_argument("--mmas", action="store_true")
parser.add_argument("--bwas", action="store_true")
parser.add_argument("--acs", action="store_true")
parser.add_argument("--quiet", action="store_true")
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_arguments()
heuristic = Metaheuristic(args)
tsplibfile = args.tsplibfile
instance = TSP(tsplibfile)
n = instance.n
heuristic.update_parameters(n)
nn = max(heuristic.nn_ls, heuristic.nn_ants)
instance.compute_nn_lists(nn)
instance.report("instance_report.txt")
ant_colony = AntColony(heuristic, instance)
ant_colony.report("ant_colony_report.txt")
pheromone_filename = sys.argv[1]
input_copy_filename = sys.argv[2]
file_pheromone = open(pheromone_filename, "r")
file_input_copy = open(input_copy_filename, "r")
for pheromone in file_pheromone:
new_pheromone = pheromone
for input_copy in file_input_copy:
dic = input_copy.strip()
dic = json.loads(dic)
new_pheromone = json.loads(new_pheromone)
dic['distances'] = np.asarray(dic['distances'])
dic['pheromones'] = np.asarray(new_pheromone['pheromones'])
ant_colony = AntColony(dic['distances'], dic['n_ants'], dic['n_iterations'],
dic['decay'], dic['alpha'], dic['beta'],
dic['pheromones'])
ant_colony.update_pheromone()
dic['distances'] = np.array(dic['distances']).tolist()
dic['pheromones'] = dic['pheromones'].tolist()
#print(dic['pheromones'])
with open('./' + sys.argv[2], 'w') as file:
file.write(json.dumps(dic))
import sys
import ast
from aco import AntColony
import numpy as np
import json
start_num = 1
end_num = 90
init_pheromone = []
for line in sys.stdin:
line = line.strip()
splits_number, dic = line.split('\t', 1)
dic = json.loads(dic)
dic['distances'] = np.asarray(dic['distances'])
dic['pheromones'] = np.asarray(dic['pheromones'])
if init_pheromone == []:
init_pheromone = dic['pheromones']
#dic['distances'] = eval(dic['distances'])
ant_colony = AntColony(dic['distances'], dic['n_ants'],
dic['n_iterations'], dic['decay'], dic['alpha'],
dic['beta'], dic['pheromones'])
#ant_colony['pheromones'] = dic['pheromones']
new_pheromone = ant_colony.run_reducer(start_num, end_num)
init_pheromone = np.add(init_pheromone, new_pheromone)
out_pheromone = {'pheromones': init_pheromone.tolist()}
#out_pheromone = {'pheromones':init_pheromone}
print('%s' % json.dumps(out_pheromone))
import numpy as np
import time
start_num = 1
end_num = 90
all_time_pheromones = []
all_time_shortest_path = [None, np.inf]
for line in sys.stdin:
line = line.strip()
shortest_path, dic = line.split('\t', 1)
dic = json.loads(dic)
new_pheromone = np.asarray(dic['pheromones'])
shortest_path = eval(shortest_path)
if all_time_shortest_path[1] > shortest_path[1]:
all_time_shortest_path = shortest_path
if all_time_pheromones == []:
all_time_pheromones = new_pheromone
else:
all_time_pheromones = np.add(all_time_pheromones, new_pheromone)
dic['distances'] = np.asarray(dic['distances'])
ant_colony = AntColony(dic['distances'], dic['n_ants'], 1, dic['decay'],
dic['alpha'], dic['beta'], all_time_pheromones)
shortest_path, new_pheromone = ant_colony.run(start_num, end_num)
if all_time_shortest_path[1] > shortest_path[1]:
all_time_shortest_path = shortest_path
print(all_time_shortest_path)