class Ui_MainWindow(object):
def __init__(self):
self.__eaAverage = []
self.__psoAverage = []
def animate_EA(self, x_vals, y_vals, index, r):
population = 40
n = 3 #Statistics are for a 3x3 matrix
P = ChromozomePopulation(population, 1, n) #population = 40
pop = P.getPopulation()
r = random.randrange(1, 40)
x_vals.append(next(index))
y_vals.append(P.fitness(pop[r], n))
self.__eaAverage.append(P.fitness(pop[r], n))
plt.cla()
plt.plot(x_vals, y_vals)
plt.show()
def animate_PSO(self, x_vals, y_vals, index, r):
population = 40
n = 3 #Statistics are for a 3x3 matrix
P = ParticlePopulation(population, 1, n) #population = 40
pop = P.getPopulation()
r = random.randrange(1, 40)
particle = pop[r]
x_vals.append(next(index))
y_vals.append(particle.fitness(particle))
self.__psoAverage.append(particle.fitness(particle))
plt.cla()
plt.plot(x_vals, y_vals)
plt.show()
def statisticsEvolutionaryAlgorithm(self, n):
plt.style.use('fivethirtyeight')
x_vals = []
y_vals = []
index = count()
for run in range(30): #run = 30
r = random.randrange(1, 40)
#evaluation = 1000 (interval)
FuncAnimation(plt.gcf(),
self.animate_EA(x_vals, y_vals, index, r),
interval=1000)
plt.tight_layout()
#average display for best solution
sum_num = 0
for t in self.__eaAverage:
sum_num = sum_num + t
avg = sum_num / len(self.__eaAverage)
print("Average of fitness score is: ", avg)
#standard deviation display for best solution
deviation = statistics.stdev(self.__eaAverage)
print("Standard deviation of fitness score is: ", deviation)
def statisticsParticleSwarmOptimization(self, n):
plt.style.use('fivethirtyeight')
x_vals = []
y_vals = []
index = count()
for run in range(30): #run = 30
r = random.randrange(1, 40)
#evaluation = 1000 (interval)
FuncAnimation(plt.gcf(),
self.animate_PSO(x_vals, y_vals, index, r),
interval=1000)
plt.tight_layout()
#average display for best solution
sum_num = 0
for t in self.__psoAverage:
sum_num = sum_num + t
avg = sum_num / len(self.__psoAverage)
print("Average of fitness score is: ", avg)
#standard deviation display for best solution
deviation = statistics.stdev(self.__psoAverage)
print("Standard deviation of fitness score is: ", deviation)
def setupUi(self, MainWindow):
MainWindow.setObjectName("MainWindow")
MainWindow.resize(800, 300)
self.centralwidget = QtWidgets.QWidget(MainWindow)
self.centralwidget.setObjectName("centralwidget")
self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)
self.lineEdit.setGeometry(QtCore.QRect(210, 70, 113, 21))
self.lineEdit.setObjectName("lineEdit")
self.lineEdit_2 = QtWidgets.QLineEdit(self.centralwidget)
self.lineEdit_2.setGeometry(QtCore.QRect(210, 100, 113, 21))
self.lineEdit_2.setObjectName("lineEdit_2")
self.lineEdit_3 = QtWidgets.QLineEdit(self.centralwidget)
self.lineEdit_3.setGeometry(QtCore.QRect(210, 130, 113, 21))
self.lineEdit_3.setObjectName("lineEdit_3")
self.lineEdit_4 = QtWidgets.QLineEdit(self.centralwidget)
self.lineEdit_4.setGeometry(QtCore.QRect(210, 160, 113, 21))
self.lineEdit_4.setObjectName("lineEdit_4")
self.label = QtWidgets.QLabel(self.centralwidget)
self.label.setGeometry(QtCore.QRect(220, 20, 131, 31))
self.label.setStyleSheet("font: 18pt \".SF NS Text\";")
self.label.setObjectName("label")
self.label_2 = QtWidgets.QLabel(self.centralwidget)
self.label_2.setGeometry(QtCore.QRect(40, 70, 141, 16))
self.label_2.setObjectName("label_2")
self.label_3 = QtWidgets.QLabel(self.centralwidget)
self.label_3.setGeometry(QtCore.QRect(70, 100, 101, 16))
self.label_3.setObjectName("label_3")
self.label_4 = QtWidgets.QLabel(self.centralwidget)
self.label_4.setGeometry(QtCore.QRect(40, 130, 151, 16))
self.label_4.setObjectName("label_4")
"""
self.label_5 = QtWidgets.QLabel(self.centralwidget)
self.label_5.setGeometry(QtCore.QRect(80, 240, 181, 41))
self.label_5.setStyleSheet("font: 18pt \".SF NS Text\";")
self.label_5.setObjectName("label_5")
"""
self.label_8 = QtWidgets.QLabel(self.centralwidget)
self.label_8.setGeometry(QtCore.QRect(70, 160, 101, 16))
self.label_8.setObjectName("label_6")
"""
self.textEdit_1 = QtWidgets.QTextEdit(self.centralwidget)
self.textEdit_1.setGeometry(QtCore.QRect(60, 290, 221, 151))
self.textEdit_1.setObjectName("textEdit_1")
"""
self.pushButton = QtWidgets.QPushButton(self.centralwidget)
self.pushButton.setGeometry(QtCore.QRect(470, 180, 181, 71))
self.pushButton.setStyleSheet("font: 18pt \".SF NS Text\";")
self.pushButton.setObjectName("pushButton")
"""
self.textEdit = QtWidgets.QTextEdit(self.centralwidget)
self.textEdit.setGeometry(QtCore.QRect(460, 310, 211, 131))
self.textEdit.setObjectName("textEdit")
self.label_6 = QtWidgets.QLabel(self.centralwidget)
self.label_6.setGeometry(QtCore.QRect(500, 270, 141, 16))
self.label_6.setObjectName("label_6")
self.label_7 = QtWidgets.QLabel(self.centralwidget)
self.label_7.setGeometry(QtCore.QRect(530, 460, 60, 16))
self.label_7.setObjectName("label_7")
self.lineEdit_5 = QtWidgets.QLineEdit(self.centralwidget)
self.lineEdit_5.setGeometry(QtCore.QRect(500, 500, 113, 21))
self.lineEdit_5.setObjectName("lineEdit_5")
"""
self.pushButton_2 = QtWidgets.QPushButton(self.centralwidget)
self.pushButton_2.setGeometry(QtCore.QRect(430, 70, 113, 32))
self.pushButton_2.setObjectName("pushButton_2")
self.pushButton_3 = QtWidgets.QPushButton(self.centralwidget)
self.pushButton_3.setGeometry(QtCore.QRect(570, 70, 113, 32))
self.pushButton_3.setObjectName("pushButton_3")
self.pushButton_4 = QtWidgets.QPushButton(self.centralwidget)
self.pushButton_4.setGeometry(QtCore.QRect(510, 120, 113, 32))
self.pushButton_4.setObjectName("pushButton_4")
MainWindow.setCentralWidget(self.centralwidget)
self.menubar = QtWidgets.QMenuBar(MainWindow)
self.menubar.setGeometry(QtCore.QRect(0, 0, 800, 22))
self.menubar.setObjectName("menubar")
MainWindow.setMenuBar(self.menubar)
self.statusbar = QtWidgets.QStatusBar(MainWindow)
self.statusbar.setObjectName("statusbar")
MainWindow.setStatusBar(self.statusbar)
self.pushButton_2.clicked.connect(self.startEA)
self.pushButton_3.clicked.connect(self.startHC)
self.pushButton_4.clicked.connect(self.startPSO)
self.pushButton.clicked.connect(self.stopAlgo)
self.retranslateUi(MainWindow)
QtCore.QMetaObject.connectSlotsByName(MainWindow)
def stopAlgo(self):
app.quit()
def startEA(self):
def callbackEA(a, b, c, d):
self.__n = d
self.__problem = Problem(self.__n)
self.__controller = Controller(self.__problem)
#startClock = time()
noIteratii = c
dimPopulation = b
# numbers for permutations will be in interval [1, n]
vmin = 1
vmax = self.__n
# the mutation probability
pM = a
# maximum fitness possible
maximumFitness = 4 * vmax
optimIndividual, fitnessOptim = self.__controller.EvolutionaryAlgorithm(
noIteratii, dimPopulation, vmin, vmax, pM)
# output of the optim individual
print("This is the best individual I could find: \n")
print(optimIndividual)
# output of the fitness of the individuali
ratio = str(fitnessOptim) + '/' + str(maximumFitness)
print(ratio)
#print('execution time = ', time() - startClock, " seconds")
lineEdit1 = float(self.lineEdit.text())
lineEdit2 = int(self.lineEdit_2.text())
lineEdit3 = int(self.lineEdit_3.text())
lineEdit4 = int(self.lineEdit_4.text())
t = threading.Thread(target=callbackEA,
args=(
lineEdit1,
lineEdit2,
lineEdit3,
lineEdit4,
))
t.start()
def startHC(self):
def callbackHC(d):
self.__n = d
self.__problem = Problem(self.__n)
self.__controller = Controller(self.__problem)
#startClock = time()
# maximum fitness possible
maximumFitness = 4 * self.__n
optimIndividual, fitnessOptim = self.__controller.HillClimbing(
self.__n)
# output of the optim individual
print("This is the best individual I could find: \n")
print(optimIndividual)
# output of the fitness of the individual
ratio = str(fitnessOptim) + '/' + str(maximumFitness)
print(ratio)
#print('execution time = ', time() - startClock, " seconds")
lineEdit4 = int(self.lineEdit_4.text())
t = threading.Thread(target=callbackHC, args=(lineEdit4, ))
t.start()
def startPSO(self):
def callbackPSO(b, c, d):
self.__n = d
self.__problem = Problem(self.__n)
self.__controller = Controller(self.__problem)
#startClock = time()
noIteratii = c
dimPopulation = b
# numbers for permutations will be in interval [1, n]
vmin = 1
vmax = self.__n
# maximum fitness possible
maximumFitness = 4 * vmax
# specific parameters for PSO
w = 1.0
c1 = 1.
c2 = 2.5
sizeOfNeighborhood = 20
optimIndividual, fitnessOptim = self.__controller.ParticleSwarmOptimization(
noIteratii, dimPopulation, vmin, vmax, w, c1, c2,
sizeOfNeighborhood)
# output of the optim individual
print("This is the best individual I could find: \n")
print(str(optimIndividual))
# output of the fitness of the individuali
ratio = str(fitnessOptim) + '/' + str(maximumFitness)
print(ratio)
#print('execution time = ', time() - startClock, " seconds")
lineEdit2 = int(self.lineEdit_2.text())
lineEdit3 = int(self.lineEdit_3.text())
lineEdit4 = int(self.lineEdit_4.text())
t = threading.Thread(target=callbackPSO,
args=(
lineEdit2,
lineEdit3,
lineEdit4,
))
t.start()
def retranslateUi(self, MainWindow):
_translate = QtCore.QCoreApplication.translate
MainWindow.setWindowTitle(_translate("MainWindow", "MainWindow"))
self.label.setText(_translate("MainWindow", "User input:"))
self.label_2.setText(
_translate("MainWindow", "Probability of mutation"))
self.label_3.setText(_translate("MainWindow", "Population size"))
self.label_4.setText(_translate("MainWindow",
"Numbers of generations"))
#self.label_5.setText(_translate("MainWindow", "Best found until now:"))
self.pushButton.setText(_translate("MainWindow", "Stop algorithm"))
#self.label_6.setText(_translate("MainWindow", "Final found solution:"))
#self.label_7.setText(_translate("MainWindow", "Fitness:"))
self.label_8.setText(_translate("MainWindor", "Size of matrix:"))
self.pushButton_2.setText(_translate("MainWindow", "Start EA"))
self.pushButton_3.setText(_translate("MainWindow", "Start HC"))
self.pushButton_4.setText(_translate("MainWindow", "Start PSO"))
class UI:
def __init__(self, n):
self.__problem = Problem(n)
self.__controller = Controller(self.__problem)
self.__eaAverage = []
self.__psoAverage = []
def particleSwarmOptimization(self):
startClock = time()
noIteratii = int(input("Enter the number of iterations = "))
dimPopulation = int(input("Enter the dimension of the population = "))
# numbers for permutations will be in interval [1, n]
vmin = 1
vmax = n
# maximum fitness possible
maximumFitness = 4 * vmax
# specific parameters for PSO
w = 1.0
c1 = 1.
c2 = 2.5
sizeOfNeighborhood = 20
optimIndividual, fitnessOptim = self.__controller.ParticleSwarmOptimization(noIteratii, dimPopulation, vmin, vmax, w, c1, c2, sizeOfNeighborhood)
print ('Result: The detected matrix after ', noIteratii, ' iterations is: ')
print(optimIndividual)
print (' with fitness optim = ', fitnessOptim, '/', maximumFitness)
print('execution time = ', time() - startClock, " seconds")
def animate_EA(self, x_vals, y_vals, index, r):
population = 40
P = ChromozomePopulation(population, 1, n) #population = 40
pop = P.getPopulation()
r = random.randrange(1, 40)
x_vals.append(next(index))
y_vals.append(P.fitness(pop[r], n))
self.__eaAverage.append(P.fitness(pop[r], n))
plt.cla()
plt.plot(x_vals, y_vals)
plt.show()
def animate_PSO(self, x_vals, y_vals, index, r):
population = 40
P = ParticlePopulation(population, 1, n) #population = 40
pop = P.getPopulation()
r = random.randrange(1, 40)
particle = pop[r]
x_vals.append(next(index))
y_vals.append(particle.fitness(particle))
self.__psoAverage.append(particle.fitness(particle))
plt.cla()
plt.plot(x_vals, y_vals)
plt.show()
def statisticsEvolutionaryAlgorithm(self, n):
plt.style.use('fivethirtyeight')
x_vals = []
y_vals = []
index = count()
for run in range(30): #run = 30
r = random.randrange(1, 40)
#evaluation = 1000 (interval)
FuncAnimation(plt.gcf(), self.animate_EA(x_vals, y_vals, index, r), interval = 1000)
plt.tight_layout()
#average display for best solution
sum_num = 0
for t in self.__eaAverage:
sum_num = sum_num + t
avg = sum_num / len(self.__eaAverage)
print ("Average of fitness score is: ", avg)
#standard deviation display for best solution
deviation = statistics.stdev(self.__eaAverage)
print ("Standard deviation of fitness score is: ", deviation)
def statisticsParticleSwarmOptimization(self, n):
plt.style.use('fivethirtyeight')
x_vals = []
y_vals = []
index = count()
for run in range(30): #run = 30
r = random.randrange(1, 40)
#evaluation = 1000 (interval)
FuncAnimation(plt.gcf(), self.animate_PSO(x_vals, y_vals, index, r), interval = 1000)
plt.tight_layout()
#average display for best solution
sum_num = 0
for t in self.__psoAverage:
sum_num = sum_num + t
avg = sum_num / len(self.__psoAverage)
print ("Average of fitness score is: ", avg)
#standard deviation display for best solution
deviation = statistics.stdev(self.__psoAverage)
print ("Standard deviation of fitness score is: ", deviation)
def evolutionaryAlgorithm(self):
startClock = time()
noIteratii = int(input("Enter the number of iterations = "))
dimPopulation = int(input("Enter the dimension of the population = "))
# numbers for permutations will be in interval [1, n]
vmin = 1
vmax = n
# the mutation probability
pM = float(input("Enter the mutation probability = "))
# maximum fitness possible
maximumFitness = 4 * vmax
optimIndividual, fitnessOptim = self.__controller.EvolutionaryAlgorithm(noIteratii, dimPopulation, vmin, vmax, pM)
print ('Result: The detected matrix after ', noIteratii, ' iterations is: ')
print(optimIndividual)
print (' with fitness optim = ', fitnessOptim, '/', maximumFitness)
print('execution time = ', time() - startClock, " seconds")
def hillClimbing(self):
startClock = time()
# maximum fitness possible
maximumFitness = 4 * n
optimIndividual, fitnessOptim = self.__controller.HillClimbing(n)
print ('Result: The detected matrix is: ')
print(optimIndividual)
print (' with fitness optim = ', fitnessOptim, '/', maximumFitness)
print('execution time = ', time() - startClock, " seconds")
def run(self):
runM = True
printMainMenu()
while runM:
command = int(input(">>"))
if command == 0:
runM = False
elif command == 1:
self.statisticsEvolutionaryAlgorithm(n)
#self.evolutionaryAlgorithm()
elif command == 2:
self.hillClimbing()
elif command == 3:
self.statisticsParticleSwarmOptimization(n)
#self.particleSwarmOptimization()
else:
print ("Invalid command")
