class UI(QWidget):
def __init__(self):
self.__problem = Problem()
self.__controller = Controller(self.__problem)
super().__init__()
#self.validation()
self.initUI()
def initUI(self):
self.l0 = QLabel(self)
self.l0.setText("1.EA;2.HC;3.PSO")
self.l0.move(0, 210)
self.le0 = QLineEdit(self)
self.le0.move(150, 210)
self.l1 = QLabel(self)
self.l1.setText("Probability of mutation")
self.l1.move(0, 0)
self.le1 = QLineEdit(self)
self.le1.move(150, 0)
#
self.l2 = QLabel(self)
self.l2.setText("Population size")
self.l2.move(0, 30)
self.le2 = QLineEdit(self)
self.le2.move(150, 30)
#
self.l3 = QLabel(self)
self.l3.setText("Number of iterations")
self.l3.move(0, 60)
self.le3 = QLineEdit(self)
self.le3.move(150, 60)
#
self.l4 = QLabel(self)
self.l4.setText("Number of neighbours")
self.l4.move(0, 90)
self.le4 = QLineEdit(self)
self.le4.move(150, 90)
#
self.l5 = QLabel(self)
self.l5.setText("w")
self.l5.move(0, 120)
self.le5 = QLineEdit(self)
self.le5.move(150, 120)
#
self.l6 = QLabel(self)
self.l6.setText("c1")
self.l6.move(0, 150)
self.le6 = QLineEdit(self)
self.le6.move(150, 150)
#
self.l7 = QLabel(self)
self.l7.setText("c2")
self.l7.move(0, 180)
self.le7 = QLineEdit(self)
self.le7.move(150, 180)
self.qtable = QTableWidget(self)
self.qtable.move(200, 300)
self.qtable.setGeometry(200, 300, 600, 600)
self.setWindowTitle('Input dialog')
self.btn = QPushButton('Take arguments', self)
self.btn.move(200, 250)
self.btn.clicked.connect(self.showDialog)
self.show()
def showDialog(self):
if isinstance(self.le1.text(), str) and self.le1.text() != '':
pM = float(self.le1.text())
else:
pM = 0
if isinstance(self.le2.text(), str) and self.le2.text() != '':
dimPopulation = int(self.le2.text())
else:
dimPopulation = 20
if isinstance(self.le3.text(), str) and self.le3.text() != '':
noIteratii = int(self.le3.text())
else:
noIteratii = 1000
if isinstance(self.le4.text(), str) and self.le4.text() != '':
sizeOfNeighborhood = int(self.le4.text())
else:
sizeOfNeighborhood = 2
if isinstance(self.le5.text(), str) and self.le5.text() != '':
w = float(self.le5.text())
else:
w = 1
if isinstance(self.le6.text(), str) and self.le6.text() != '':
c1 = float(self.le6.text())
else:
c1 = 1
if isinstance(self.le7.text(), str) and self.le7.text() != '':
c2 = float(self.le7.text())
else:
c2 = 2.5
if isinstance(self.le0.text(), str) and self.le0.text() != '':
a = self.le0.text()
else:
a = 0
self.run(a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w, c1,
c2)
def printMatrix(self, array):
self.qtable.setColumnCount(len(array[0])) # rows and columns of table
self.qtable.setRowCount(len(array[0]))
for row in range(len(
array[0])): # add items from array to QTableWidget
for column in range(len(array[0])):
item = (array[0][row][column], array[1][row][column]
) # each item is a QTableWidgetItem
self.qtable.setItem(row, column, QTableWidgetItem(str(item)))
self.qtable.show()
def case1(self, a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w,
c1, c2):
dimIndividual = 4
P = self.__problem.population(dimPopulation, dimIndividual, 0, 0)
for i in range(noIteratii):
P = self.__controller.iteration(P, pM, 0, 0)
# print the best individual
graded = [(self.__problem.fitness(x), x) for x in P]
graded = sorted(graded)
result = graded[0]
fitnessOptim = result[0]
individualOptim = result[1]
self.printMatrix(individualOptim)
def case2(self, a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w,
c1, c2):
dimIndividual = 4
ind = self.__problem.individual(dimIndividual, 0, 0)
res = self.__controller.hillClimb(ind)
self.printMatrix(res)
def case3(self, a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w,
c1, c2):
noParticles = dimPopulation
# individual size
dimParticle = 4
# the boundries of the search interval
vmin = -100
vmax = -10
# specific parameters for PSO
w = 1.0
c1 = 1.
c2 = 2.5
# sizeOfNeighborhood = 2
P = self.__problem.populationForParticles(noParticles, dimParticle,
vmin, vmax)
# we establish the particles' neighbors
neighborhoods = self.__problem.selectNeighbors(P, sizeOfNeighborhood)
for i in range(noIteratii):
P = self.__controller.iterationForParticles(
P, neighborhoods, c1, c2, w / (i + 1))
# print the best individual
best = 0
for i in range(1, len(P)):
if (P[i].fitness < P[best].fitness):
best = i
fitnessOptim = P[best].fitness
individualOptim = P[best].pozition
self.printMatrix(individualOptim)
def run(self, a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w, c1,
c2): #ui
dimIndividual = 4
vmin = 0
vmax = 0
if a == "1":
self.case1(a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w,
c1, c2)
if a == "2":
self.case2(a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w,
c1, c2)
if a == "3":
self.case3(a, pM, dimPopulation, noIteratii, sizeOfNeighborhood, w,
c1, c2)
def validation(self):
fitnessOptimForEA = []
for i in range(30):
dimIndividual = 4
dimPopulation = 40
noIteratii = 1000
pM = 0.01
P = self.__problem.population(dimPopulation, dimIndividual, 0, 0)
for i in range(noIteratii):
P = self.__controller.iteration(P, pM, 0, 0)
graded = [(self.__problem.fitness(x), x) for x in P]
graded = sorted(graded)
result = graded[0]
fitnessOptimForEA.append(result[0])
fitnessOptimForHC = []
for i in range(30):
dimIndividual = 4
ind = self.__problem.individual(dimIndividual, 0, 0)
res = self.__controller.hillClimb(ind)
fitnessOptimForHC.append(self.__problem.fitness(res))
fitnessOptimForPSO = []
for i in range(3):
noIteratii = 1000
noParticles = 40
dimParticle = 4
# the boundries of the search interval
vmin = -100
vmax = -10
# specific parameters for PSO
w = 1.0
c1 = 1.
c2 = 2.5
sizeOfNeighborhood = 2
P = self.__problem.populationForParticles(noParticles, dimParticle,
vmin, vmax)
neighborhoods = self.__problem.selectNeighbors(
P, sizeOfNeighborhood)
for i in range(noIteratii):
P = self.__controller.iterationForParticles(
P, neighborhoods, c1, c2, w / (i + 1))
best = 0
for i in range(1, len(P)):
if (P[i].fitness < P[best].fitness):
best = i
fitnessOptim = P[best].fitness
fitnessOptimForPSO.append(fitnessOptim)
plt.plot(fitnessOptimForEA) # plotting by columns
np1 = np.array(fitnessOptimForEA)
std1 = np.std(np1)
m1 = np.mean(np1)
plt.xlabel("standard deviation= " + str(std1) + ";mean= " + str(m1) +
" Trials")
plt.ylabel("EA algorithm" + " Fitness")
plt.show()
plt.plot(fitnessOptimForHC) # plotting by columns
np2 = np.array(fitnessOptimForHC)
std2 = np.std(np2)
m2 = np.mean(np2)
plt.xlabel("standard deviation= " + str(std2) + ";mean= " + str(m2) +
" Trials")
plt.ylabel("HC algorithm" + " Fitness")
plt.show()
plt.plot(fitnessOptimForPSO) # plotting by columns
np3 = np.array(fitnessOptimForPSO)
std3 = np.std(np3)
m3 = np.mean(np3)
plt.xlabel("standard deviation= " + str(std3) + ";mean= " + str(m3) +
" Trials")
plt.ylabel("PSO algorithm" + " Fitness")
plt.show()
