def test_alpha_das_qas_less(self): # alpha = 0.1
aco_das = aco.ACO('Kempten',
'Wuerzburg',
ants_num=50,
iteration_num=10,
type='das',
rho=0.4,
beta=1,
alpha=0.1) # rho = 0.9
aco_qas = aco.ACO('Kempten',
'Wuerzburg',
ants_num=50,
iteration_num=10,
type='qas',
rho=0.4,
beta=1,
alpha=0.1)
t1_start = process_time()
aco_das.aco_run()
t1_stop = process_time()
t1 = t1_stop - t1_start
t2_start = process_time()
aco_qas.aco_run()
t2_stop = process_time()
t2 = t2_stop - t2_start
self.assertLessEqual(t1, t2)
def test_def_das_qas_less(self): # rho = 0.5
aco_das = aco.ACO('Kempten',
'Wuerzburg',
ants_num=50,
iteration_num=10,
type='das')
aco_qas = aco.ACO('Kempten',
'Wuerzburg',
ants_num=50,
iteration_num=10,
type='qas')
t1_start = process_time()
aco_das.aco_run()
t1_stop = process_time()
t1 = t1_stop - t1_start
t2_start = process_time()
aco_qas.aco_run()
t2_stop = process_time()
t2 = t2_stop - t2_start
self.assertLessEqual(t1, t2)
def main():
p, clients = get_data(args.INPUT_FILE)
p_medians = aco.ACO(p, clients, args.MAXIT, args.ANTN, args.DECAYR,
args.ALPHA, args.BETA)
p_medians.ant_system()
print(p_medians.best_global)
from plot import Plot
import numpy as np
from matrices import matrices, all_num_optional_nodes
matrices_size = ["Small Graph", "Medium Graph", "Large Graph", "XL Graph"]
i = 0
for matrix, num_optional_nodes in zip(matrices, all_num_optional_nodes):
whole_matrix_rank = len(matrix)
classical_matrix_rank = whole_matrix_rank - num_optional_nodes
m = np.array(matrix)
classical_matrix = m[0:classical_matrix_rank, 0:classical_matrix_rank]
optional_nodes = list(range(classical_matrix_rank, whole_matrix_rank))
g = aco.Graph(classical_matrix)
alg = aco.ACO(10, 20, 1, 1, 0.5, 1, 0)
best_solution, best_cost, avg_costs, best_costs, plot_x, plot_y = alg.solve(
g, True)
# new_plot=Plot("Regular ACO" ,['r', 'g'],plot_x,"cost")
# new_plot.plot_lines(plot_y)
# new_plot.display()
g0 = aco_max.Graph(classical_matrix)
alg0 = aco_max.ACO_Max(10, 20, 1, 1, 0.5, 1, 0)
best_solution0, best_cost0, avg_costs0, best_costs0, plot_x0, plot_y0 = alg0.solve(
g0, True)
# new_plot=Plot("Max ACO",['r', 'g'],plot_x0,"cost")
# new_plot.plot_lines(plot_y0)
# new_plot.display()
import aco
import numpy as np
import pandas as pd
dist_mat = pd.read_csv('tc4.csv', header=None).values
g = aco.Graph(dist_mat)
ac = aco.ACO(g, num_colony=10,iter=5,beta=3,rho=0.5)
sol = ac.solve_step()
cost_over_time = []
for i in sol:
final = i
cost_over_time.append(i.travel_dist)
thefile = open('cost_over_time4.txt', 'w')
for item in cost_over_time:
thefile.write("%s\n" % item)
thefile.close()
thefile = open('solution4.txt', 'w')
for item in final.visited_city:
thefile.write("%s\n" % item)
thefile.close()