def Global_Pollination(self, popu, BestValue):
pop = []
step = []
for i in range(self.func.dimension):
step.append(levy.pdf(1.5))
for i in range(self.func.dimension):
temp = popu[i] + step[i] * (BestValue[i] - popu[i])
pop.append(temp)
return pop
def butterfly_adjusting_operator(subpopulation2, Xbest, Maxgen, Smax, t, p,
BAR, v, w): #return subpopulation2
NP2 = subpopulation2.shape[0]
d = int((subpopulation2.shape[1] - 2) / 2)
dx = np.empty(d)
omega = Smax / (t**2) #t is the current generation
for i in range(d):
StepSize = math.ceil(np.random.exponential(2 * Maxgen))
dx[i] = levy.pdf(StepSize)
for i in range(NP2): #for all monarch in subpopulation 2
for j in range(d): # for all elements in monarch
rand = np.random.uniform(0, 1)
if rand <= p:
subpopulation2[i, j] = Xbest[j]
else:
r3 = np.random.randint(0, NP2)
subpopulation2[i, j] = subpopulation2[r3, j]
if rand > BAR:
subpopulation2[
i, j] = subpopulation2[i, j] + omega * (dx[j] - 0.5)
update_array(subpopulation2, v, w)
return subpopulation2
from scipy.stats import levy import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) # Calculate a few first moments: mean, var, skew, kurt = levy.stats(moments='mvsk') # Display the probability density function (``pdf``): x = np.linspace(levy.ppf(0.01), levy.ppf(0.99), 100) ax.plot(x, levy.pdf(x), 'r-', lw=5, alpha=0.6, label='levy pdf') # Alternatively, the distribution object can be called (as a function) # to fix the shape, location and scale parameters. This returns a "frozen" # RV object holding the given parameters fixed. # Freeze the distribution and display the frozen ``pdf``: rv = levy() ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') # Check accuracy of ``cdf`` and ``ppf``: vals = levy.ppf([0.001, 0.5, 0.999]) np.allclose([0.001, 0.5, 0.999], levy.cdf(vals)) # True # Generate random numbers:
from scipy.stats import levy print(levy.pdf(6, 3, 3))
from scipy.stats import levy print(levy.pdf(6,3,3))