def doGridsearchExperiment():
np.set_printoptions(formatter={'float': lambda x: "{0:0.2f}".format(x)})
instances_directory = 'instances/'
instances_directory = 'testinstances/'
# instances_directory = 'from_assignment_3/'
opt_directory = 'opts/'
# instancename = "maxcut_4x4_1_1_donut"
# instancename = "maxcut_2x2_1_1_donut"
instancename = 'newL25_2' #opt = 530
# instancename = 'newL12_1' # opt = 127
# instancename = 'newL12_2' # opt = 124
instancename = "newL50_1_opt2078"
# instancename = "newL50_2_opt2056"
instancename = "maxcut_random_128_0.4_100_instance_3"
instance = maxcut.MaxCut(instancename + ".txt", instances_directory,
opt_directory)
numAnts = 20
max_evals = 1000000
max_gens = 200
X = 10
# For GBO:
# rho_list = np.arange(0.01,0.51,0.1)
# alpha_list = np.arange(0.75,2,0.25)
# alpha_list = np.arange(0.5,1.5,0.1)
# For BBO:
# rho_list = np.arange(0.01,0.51,0.1)
# alpha_list = np.arange(0.6,1.1,0.1)
# For BBO2, in neighbourhood of:
# rho = 0.2
# ph_max=100
# ph_min=0.1
# alpha = 0.7
rho_list = np.arange(0.15, 0.4, 0.05)
alpha_list = np.arange(0.6, 1, 0.1)
beta_list = [1]
ph_min_list = [0.1]
ph_max_list = [100]
rn.gridSearch(X,
instance,
numAnts,
max_evals,
max_gens,
rho_list,
alpha_list,
beta_list,
ph_min_list,
ph_max_list,
BBO=True)
plt.show()
def doGlobalTest():
np.set_printoptions(formatter={'float': lambda x: "{0:0.2f}".format(x)})
instances_directory = 'testinstances/'
opt_directory = 'opts/'
instancenamelist = [
"maxcut_random_16_0.4_100_instance_0",
"maxcut_random_32_0.4_100_instance_1",
"maxcut_random_64_0.4_100_instance_2",
"maxcut_random_128_0.4_100_instance_3",
"maxcut_random_256_0.4_100_instance_4",
"maxcut_random_512_0.4_100_instance_5"
]
# instancenamelist = ["maxcut_random_16_0.4_100_instance_0","maxcut_random_32_0.4_100_instance_1"]
plt.ion()
f = open("ACOend_results.txt", "a")
f.write(
"filename \t\t\t\t\t\t\t evaluations \t mean \t std \t maxfitness \n")
# f2 = open("ACOrun_results.txt","w")
for instancename in instancenamelist:
fig, ax = plt.subplots()
instance = maxcut.MaxCut(instancename + ".txt", instances_directory,
opt_directory)
numAnts = 20
max_evals = 10000000000
max_gens = 200
# best parameters for instance newL50_1_opt2078 without local search, rho = 0.2
rho = 0.3 # result for with local search on N=128, 0.3 or 0.35
ph_max = 100
ph_min = 0.1
alpha = 0.7
ACO_instance = ACO.ACO_BBO2(instance, numAnts, max_evals, max_gens,
rho, ph_max, ph_min, alpha)
X = 10
x, y, e, maxfit = rn.executeXtimes(X, ACO_instance)
ax.errorbar(x, y, e, linestyle='None', marker="x", label=instancename)
ax.legend(loc="lower right")
plt.draw(), plt.pause(1e-4)
f.write("%s \t %.0f \t %.2f \t %.5f \t %.0f \n" %
(instancename, x[-1], y[-1], e[-1], maxfit))
f.flush()
# f.write("end mean: %.2f \n" % y[-1])
# f.write("end std: %.5f \n" % e[-1])
# f.write("best fitness found: %.0f" % maxfit)
plt.ioff()
f.close()
plt.show()
import numpy as np
from random import randint
import maxcut_acoBBO
import math
from operator import attrgetter
if __name__ == "__main__":
np.set_printoptions(formatter={'float': lambda x: "{0:0.2f}".format(x)})
instances_directory = 'instances/'
# instances_directory = 'from_assignment_3/'
opt_directory = 'opts/'
instancename = "maxcut_4x4_1_1_donut"
# instancename = "maxcut_2x2_1_1_donut"
# instancename = 'newL25_2' #opt = 530
instance = maxcut.MaxCut(instancename + ".txt", instances_directory,
opt_directory)
numAnts = 4
max_its = 50
rho = 1 - 0.01**(1 / (max_its / 4))
ph_max = 2
ph_min = 1
alpha = 1
ACO = maxcut_acoBBO.ACO_BBO(instance, numAnts, max_its, rho, ph_max,
ph_min, alpha)
ACO.run()
class ACO_BBO:
def __init__(self,
instance,
numAnts,