class Controller:
def __init__(self, problemFile, paramFile, toBe=False):
self._problem = Problem(problemFile)
self._dimParticle = len(self._problem.getVertices())
self._noPopulation, self._sizeOfNeighborhood, self._w, self._c1, self._c2 = 0, 0, 0, 0, 0
self.loadParameters(paramFile)
self._population = Swarm(self._noPopulation, self._dimParticle,
self._problem)
self._neighborhoods = self.selectNeighbors()
self._toBeDrawn = toBe
def getPopulation(self):
return self._population
def setPopulation(self):
return self._population
def selectNeighbors(self):
pop = self._population
nSize = self._sizeOfNeighborhood
if (nSize > len(pop)):
nSize = len(pop)
# Attention if nSize==len(pop) this selection is not a propper one
# use a different approach (like surfle to form a permutation)
neighbors = []
for i in range(len(pop)):
localNeighbor = []
for j in range(nSize):
x = randint(0, len(pop) - 1)
while (x in localNeighbor):
x = randint(0, len(pop) - 1)
localNeighbor.append(x)
neighbors.append(localNeighbor.copy())
return neighbors
def sigmoidFunction(self, x):
return 1 / (1 + exp(-x))
def iteration(self):
pop, neighbors, w, c1, c2 = self._population.getListOfParticles(
), self._neighborhoods, self._w, self._c1, self._c2
bestNeighbors = []
# determine the best neighbor for each particle
for i in range(len(pop)):
bestNeighbors.append(neighbors[i][0])
for j in range(1, len(neighbors[i])):
if (pop[bestNeighbors[i]].fitness >
pop[neighbors[i][j]].fitness):
bestNeighbors[i] = neighbors[i][j]
# update the velocity for each particle
for i in range(len(pop)):
for j in range(len(pop[0].velocity)):
newVelocity = w * pop[i].velocity[j]
newVelocity = newVelocity + c1 * random() * (
pop[bestNeighbors[i]].position[j] - pop[i].position[j])
newVelocity = newVelocity + c2 * random() * (
pop[i].bestPosition[j] - pop[i].position[j])
pop[i].velocity[j] = newVelocity
# update the position for each particle
for i in range(len(pop)):
newposition = []
for j in range(len(pop[i].velocity)):
if (random() >= self.sigmoidFunction(pop[i].velocity[j])):
newposition.append(1)
else:
newposition.append(2)
pop[i].position = newposition
self._population.setListOfParticles(pop)
return self._population
def runAlgorithm(self):
for i in range(self._noPopulation):
self._population = self.iteration()
best = 0
fitnesses = []
P = self._population.getListOfParticles()
for i in range(0, len(self._population)):
fitnesses.append(P[i].fitness)
if (P[i].fitness < P[best].fitness):
best = i
if self._toBeDrawn:
pyp.plot(fitnesses)
pyp.show()
print('The best particle has: \n' + str(P[best]) + 'Fitness = ' +
str(P[best].fitness) + '\n')
def loadParameters(self, paramFile):
f = open(paramFile)
line = f.readline().split()
self._noPopulation, self._sizeOfNeighborhood, self._w, self._c1, self._c2 = \
int(line[0]), int(line[1]), float(line[2]), float(line[3]), float(line[4])
f.close()
