def __init__(self,range1, nrParticles,maxIterations, chosenFunction):
self.chosenFunction = chosenFunction
self.range = range1
self.maxIterations = maxIterations
self.hive = []
self.alpha = 0.72
self.T = 100
for i in range(0,nrParticles,1):
x = np.random.uniform(low= -self.range, high = self.range)
y = np.random.uniform(low= -self.range, high = self.range)
fitness = returnChosenFunction(x, y,self.chosenFunction)
self.hive.append(Particle(x, y, fitness))
def __init__(self, range1, nrVectors, maxIterations, chosenFunction):
self.chosenFunction = chosenFunction
self.maxIterations = maxIterations
self.range = range1
self.nrVectors = nrVectors
self.hive = []
self.C = 0.5
self.F = 0.8
for i in range(0, nrVectors, 1):
x = np.random.uniform(low=-self.range, high=self.range)
y = np.random.uniform(low=-self.range, high=self.range)
z = returnChosenFunction(x, y, self.chosenFunction)
self.hive.append(Vector3D(x=x, y=y, fitness=z, whichConstructor=0))
def moveStar(self):
for i in range(0, self.nrStars, 1):
if (((self.hive[i].x + self.hive[i].velocity.x) < -self.range) or
((self.hive[i].x + self.hive[i].velocity.x) > self.range)):
self.hive[i].x = np.random.uniform(low=-self.range,
high=self.range)
else:
self.hive[i].x += self.hive[i].velocity.x
if (((self.hive[i].y + self.hive[i].velocity.y) < -self.range) or
((self.hive[i].y + self.hive[i].velocity.y) > self.range)):
self.hive[i].y = np.random.uniform(low=-self.range,
high=self.range)
else:
self.hive[i].y += self.hive[i].velocity.y
self.hive[i].fitness = -returnChosenFunction(
self.hive[i].x, self.hive[i].y, self.chosenFunction)
def startAlgorithm(self, whichAlgorithm):
self.whichAlgorithm = whichAlgorithm
self.closePlotWindow = False
fig = plt.figure(1)
fig.canvas.mpl_connect('close_event', self.quit_figure)
ax = fig.add_subplot(111)
u = np.linspace(-int(self.agentsRange), int(self.agentsRange), 100)
x, y = np.meshgrid(u, u)
z = returnChosenFunction(x, y, self.chosenFunction)
plt.xlim(-int(self.agentsRange), int(self.agentsRange))
plt.ylim(-int(self.agentsRange), int(self.agentsRange))
ax.contourf(x, y, z)
self.createHive()
self.timer = Clock.schedule_interval(partial(self.timerFunction),
float(self.interval))
plt.show(block=False)
def __init__(self,range1,nrAnts,maxIterations,chosenFunction):
self.chosenFunction = chosenFunction
self.hive = []
self.maxIterations = maxIterations
self.nrAnts = nrAnts
self.range = range1
self.a_T = 5
self.hive = sorted(self.hive, key=attrgetter('z'))
self.hive.reverse()
for i in range(0,self.nrAnts,1):
x = np.random.uniform(low= -self.range, high = self.range)
y = np.random.uniform(low= -self.range, high = self.range)
z = -returnChosenFunction(x,y,self.chosenFunction)
self.hive.append(Ant(x, y, z))
self.initAlhorithm()
def doOneIteration(self, iteration):
for i in range(0, self.numberOfFireflies, 1):
for j in range(0, self.numberOfFireflies, 1):
if (i == j):
continue
distance = self.distanceBetweenFireflies3D(
self.hive[i], self.hive[j])
if (self.calculateLightIntensity(self.hive[i], distance) <
self.calculateLightIntensity(self.hive[j], distance)):
attractivenessTemp = self.calculateBeta(
self.hive[i], distance)
self.alfa = 0.2 * np.power(np.random.random(), iteration)
if (attractivenessTemp < 0.000000000001):
self.hive[i].x = self.hive[i].x + self.alfa * (
np.random.random() - 0.5)
self.hive[i].y = self.hive[i].y + self.alfa * (
np.random.random() - 0.5)
else:
self.hive[i].x = self.hive[i].x + attractivenessTemp * (
self.hive[j].x -
self.hive[i].x) + self.alfa * (np.random.random())
self.hive[i].y = self.hive[i].y + attractivenessTemp * (
self.hive[j].y -
self.hive[i].y) + self.alfa * (np.random.random())
if self.hive[i].x >= self.range:
self.hive[i].x = self.range - 0.5
elif self.hive[i].x <= -self.range:
self.hive[i].x = -self.range + 0.5
if self.hive[i].y >= self.range:
self.hive[i].y = self.range - 0.5
elif self.hive[i].y <= -self.range:
self.hive[i].y = -self.range + 0.5
self.hive[i].lightIntensity = returnChosenFunction(
self.hive[i].x, self.hive[i].y, self.chosenFunction)
self.hive = sorted(self.hive, key=attrgetter('lightIntensity'))
self.hive.reverse()
def moveGlowworms(self):
for i in range(0, self.nrGlowworms, 1):
i_neighbours = []
i_neighbours_luciferin_sum = 0
for j in range(0, self.nrGlowworms, 1):
if (i != j):
d = self.distanceBetweenGlowworms(self.hive[i],
self.hive[j])
if ((d < self.hive[i].sensorRange) and
(self.hive[i].luciferin < self.hive[j].luciferin)):
i_neighbours.append(self.hive[j])
i_neighbours_luciferin_sum += (self.hive[j].luciferin -
self.hive[i].luciferin)
for j in range(0, len(i_neighbours), 1):
i_neighbours[j].moveProbability = (
i_neighbours[j].luciferin -
self.hive[i].luciferin) / i_neighbours_luciferin_sum
i_to_j_glowworm_index = self.getRandomBestGlowwormIndex(
i_neighbours)
if (i_to_j_glowworm_index != -1):
i_j_difference_X = self.hive[
i_to_j_glowworm_index].x - self.hive[i].x
self.hive[i].x += self.s * (i_j_difference_X /
np.abs(i_j_difference_X))
i_j_difference_Y = self.hive[
i_to_j_glowworm_index].y - self.hive[i].y
self.hive[i].y += self.s * (i_j_difference_Y /
np.abs(i_j_difference_Y))
self.hive[i].fitness = returnChosenFunction(
self.hive[i].x, self.hive[i].y, self.chosenFunction)
self.hive[i].sensorRange = min(
self.r_max,
max(
0, self.hive[i].sensorRange + self.beta *
(self.n_t - np.abs(len(i_neighbours)))))
def doOneIteration(self,iteration):
if (self.maxIterations >= iteration):
for i in range(0,self.nrAnts,1):
move_to_index = 0
sum_range = 0
rng_move = np.random.random()
for j in range(0,self.nrAnts-1,1):
if ((rng_move >= sum_range) and (rng_move <= (sum_range + self.hive[j + 1].probability))):
move_to_index = j + 1
break
sum_range += np.round(self.hive[j+1].probability, 3)
dx = np.random.uniform(low= 0, high = self.a_T)
if (self.hive[i].x > self.hive[move_to_index].x):
self.hive[i].x -= dx
elif (self.hive[i].x < self.hive[move_to_index].x):
self.hive[i].x += dx
else:
dx = np.random.uniform(low= -self.a_T, high = self.a_T)
self.hive[i].x += dx
dx = np.random.uniform(low= 0, high = self.a_T)
if (self.hive[i].y > self.hive[move_to_index].y):
self.hive[i].y -= dx
elif(self.hive[i].y < self.hive[move_to_index].y):
self.hive[i].y += dx
else:
dx = np.random.uniform(low= -self.a_T, high = self.a_T)
self.hive[i].y += dx
if (self.hive[i].y > self.range or self.hive[i].y < -self.range or self.hive[i].x > self.range or self.hive[i].x < -self.range):
self.hive[i].x = np.random.uniform(low= -self.range, high = self.range)
self.hive[i].y = np.random.uniform(low= -self.range, high = self.range)
self.hive[i].z = -returnChosenFunction(self.hive[i].x, self.hive[i].y,self.chosenFunction)
self.a_T *= 0.9
self.initAlhorithm()
def doOneIteration(self, iteration):
for i in range(0, self.numberOfParticles, 1):
self.hive[i].velocityX = np.random.random(
) * self.hive[i].velocityX + self.alfa * (np.random.random(
)) * (self.globalBestX - self.hive[i].x) + self.beta * (
np.random.random()) * (self.hive[i].theBestX - self.hive[i].x)
self.hive[i].velocityY = np.random.random(
) * self.hive[i].velocityY + self.alfa * (np.random.random(
)) * (self.globalBestY - self.hive[i].y) + self.beta * (
np.random.random()) * (self.hive[i].theBestY - self.hive[i].y)
oldZ = self.hive[i].z
self.hive[i].x = self.hive[i].x + self.hive[i].velocityX
self.hive[i].y = self.hive[i].y + self.hive[i].velocityY
if self.hive[i].x >= self.range:
self.hive[i].x = self.range - 0.5
elif self.hive[i].x <= -self.range:
self.hive[i].x = -self.range + 0.5
if self.hive[i].y >= self.range:
self.hive[i].y = self.range - 0.5
elif self.hive[i].y <= -self.range:
self.hive[i].y = -self.range + 0.5
x = self.hive[i].x
y = self.hive[i].y
self.hive[i].z = returnChosenFunction(x, y, self.chosenFuntion)
if (self.hive[i].z < oldZ):
self.hive[i].theBestX = self.hive[i].x
self.hive[i].theBestY = self.hive[i].y
self.hive = sorted(self.hive, key=attrgetter('z'))
if (self.globalBestZ > self.hive[0].z):
self.globalBestX = self.hive[0].x
self.globalBestY = self.hive[0].y
self.globalBestZ = self.hive[0].z
def __init__(self, range1, nrGlowworms, maxIterations, chosenFunction):
self.chosenFunction = chosenFunction
self.maxIterations = maxIterations
self.range = range1
self.hive = []
self.nrGlowworms = nrGlowworms
self.alpha = 0.4
self.beta = 0.08
self.gamma = 0.6
self.r_max = (2 * self.range) / float(3)
self.s = self.range / float(((self.range * self.range + 2)))
self.n_t = 5
for i in range(0, self.nrGlowworms, 1):
x = np.random.uniform(low=-self.range, high=self.range)
y = np.random.uniform(low=-self.range, high=self.range)
fitness = returnChosenFunction(x, y, self.chosenFunction)
sensor_range = (2 * self.range) / float(4)
self.hive.append(
Glowworm(x=x,
y=y,
fitness=fitness,
sensor_range=sensor_range,
luciferin=5))
def doOneIteration(self, iterations):
self.hive = sorted(self.hive, key=attrgetter('fitness'))
self.hive.reverse()
constG = self.initialConstG * np.exp(
-self.constBeta * (iterations / self.constMaxIterations))
delta = 0.001
if (iterations % 1) == 0:
if self.k > 3:
self.k -= 1
helpMassSum = 0
for i in range(0, self.numberOfParticles, 1):
self.hive[i].helpMass = (
self.hive[i].fitness -
self.hive[self.numberOfParticles - 1].fitness) / (
self.hive[0].fitness -
self.hive[self.numberOfParticles - 1].fitness)
helpMassSum = helpMassSum + self.hive[i].helpMass
for i in range(0, self.numberOfParticles, 1):
self.hive[i].activeMass = self.hive[i].helpMass / helpMassSum
for i in range(0, self.numberOfParticles - 1, 1):
self.hive[i].forceX = 0
self.hive[i].forceY = 0
for j in range(0, self.k, 1):
if (i != j):
self.hive[i].forceX = self.hive[
i].forceX + np.random.random() * constG * (
(self.hive[i].activeMass * self.hive[j].activeMass)
/ ((self.distanceBetweenParticles(
self.hive[i], self.hive[j]) + delta))) * (
self.hive[j].x - self.hive[i].x)
self.hive[i].forceY = self.hive[
i].forceY + np.random.random() * constG * (
(self.hive[i].activeMass * self.hive[j].activeMass)
/ ((self.distanceBetweenParticles(
self.hive[i], self.hive[j]) + delta))) * (
self.hive[j].y - self.hive[i].y)
self.hive[i].accelerationX = self.hive[i].forceX / self.hive[
i].activeMass
self.hive[i].accelerationY = self.hive[i].forceY / self.hive[
i].activeMass
self.hive[i].velocityX = (
np.random.random() *
self.hive[i].velocityX) + self.hive[i].accelerationX
self.hive[i].velocityY = (
np.random.random() *
self.hive[i].velocityY) + self.hive[i].accelerationY
if (((self.hive[i].x + self.hive[i].velocityX) < -self.range) or
((self.hive[i].x + self.hive[i].velocityX) > self.range)):
self.hive[i].x = np.random.uniform(low=-self.range,
high=self.range)
else:
self.hive[i].x = self.hive[i].x + self.hive[i].velocityX
if (((self.hive[i].y + self.hive[i].velocityY) < -self.range) or
((self.hive[i].y + self.hive[i].velocityY) > self.range)):
self.hive[i].y = np.random.uniform(low=-self.range,
high=self.range)
else:
self.hive[i].y = self.hive[i].y + self.hive[i].velocityY
self.hive[i].fitness = returnChosenFunction(
self.hive[i].x, self.hive[i].y, self.chosenFunction)
def __init__(self, x, y, chosenFunction):
self.x = x
self.y = y
self.lightIntensity = returnChosenFunction(self.x, self.y,
chosenFunction)
self.attractiveness = 1
def doOneIteration(self, iterations):
self.theBestX = self.hive[0].x
self.theBestY = self.hive[0].y
self.theBestZ = self.hive[0].z
for i in range(0, self.numberOfParticles, 1):
self.hive[i].electricCharge = (
self.hive[i].z - self.hive[self.numberOfParticles - 1].z) / (
self.hive[0].z - self.hive[self.numberOfParticles - 1].z)
helpForceX = None
helpForceY = None
probability = None
alfa = None
beta = None
for i in range(0, self.numberOfParticles, 1):
helpForceX = 0
helpForceY = 0
for j in range(0, self.numberOfParticles, 1):
if (i == j):
continue
if (((self.hive[i].z - self.hive[0].z) /
(self.hive[j].z - self.hive[i].z) > np.random.random())
or (self.hive[j].z > self.hive[i].z)):
probability = 1
else:
probability = 0
distanceBetweenParticles = self.distanceBetweenParticles3D(
self.hive[i], self.hive[j])
if (distanceBetweenParticles < self.particleDiameter):
alfa = 1
beta = 0
else:
alfa = 0
beta = 1
helpForceX += (
(alfa * self.hive[j].electricCharge / np.power(
self.particleDiameter, 3) * distanceBetweenParticles) +
(beta * self.hive[j].electricCharge /
np.power(distanceBetweenParticles, 2))
) * self.particleDiameter * distanceBetweenParticles * probability * (
self.hive[j].x - self.hive[i].x)
helpForceY += (
(alfa * self.hive[j].electricCharge / np.power(
self.particleDiameter, 3) * distanceBetweenParticles) +
(beta * self.hive[j].electricCharge /
np.power(distanceBetweenParticles, 2))
) * self.particleDiameter * distanceBetweenParticles * probability * (
self.hive[j].y - self.hive[i].y)
self.hive[i].forceX = self.hive[i].electricCharge * helpForceX
self.hive[i].forceY = self.hive[i].electricCharge * helpForceY
oldX = self.hive[i].x
oldY = self.hive[i].y
newX = (self.hive[i].x + 0.5 *
(1 - iterations / self.constIterations) *
np.random.random() * self.hive[i].velocityX) + (
0.5 * np.random.random() *
(1 + iterations / self.constIterations) *
self.hive[i].forceX)
newY = (self.hive[i].y + 0.5 *
(1 - iterations / self.constIterations) *
np.random.random() * self.hive[i].velocityY) + (
0.5 * np.random.random() *
(1 + iterations / self.constIterations) *
self.hive[i].forceY)
if (((newX + self.hive[i].velocityX) < -self.range)
or ((newX + self.hive[i].velocityX) > self.range)):
self.hive[i].x = np.random.uniform(low=-self.range,
high=self.range)
else:
self.hive[i].x = newX
if (((newY + self.hive[i].velocityY) < -self.range)
or ((newY + self.hive[i].velocityY) > self.range)):
self.hive[i].y = np.random.uniform(low=-self.range,
high=self.range)
else:
self.hive[i].y = newY
self.hive[i].velocityX = self.hive[i].x - oldX
self.hive[i].velocityY = self.hive[i].y - oldY
self.hive[i].z = returnChosenFunction(self.hive[i].x,
self.hive[i].y,
self.chosenFunction)
self.hive = sorted(self.hive, key=attrgetter('z'))
self.hive.reverse()