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rao_1.py
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rao_1.py
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# Taken from https://drive.google.com/file/d/1ytTQ8oEHN5wAeoXXveOa4SJP6y3qRAXZ/view which is found here : https://sites.google.com/view/raoalgorithms/algorithm-codes
# under constrained optimization section
import numpy as np
def Rao_1():
RUNS = 10 # Number of individual runs
for _ in range(RUNS):
pop = 5 # population size
var = 2 # Number of design variables
maxFes = 500000 # Maximum functions evaluation
maxGen = maxFes // pop # Maximum number of iterations
mini = np.array([-5, -5])
maxi = np.array([5, 5])
x = np.random.rand(pop, var) * (maxi - mini) + mini
f = objective(x)
gen = 0
fopt = []
while gen < maxGen:
xnew = update_population(x, f)
xnew = trimr(mini, maxi, xnew)
fnew = objective(xnew)
for i in range(pop):
if fnew[i] < f[i]:
x[i, :] = xnew[i, :]
f[i] = fnew[i]
print('%%%%%% Final population%%%%%%%')
print(np.hstack((x, f.reshape(-1, 1))))
gen += 1
fopt.append(np.min(f))
runs_fes = pop * (np.argmin(fopt) + 1)
runs_best = np.min(fopt)
bbest = np.min(fopt)
mbest = np.mean(fopt)
wbest = np.max(fopt)
stdbest = np.std(fopt)
mFes = runs_fes
print(f'\n best={bbest}')
print(f'\n mean={mbest}')
print(f'\n worst={wbest}')
print(f'\n std. dev.={stdbest}')
print(f'\n mean Fes={mFes}')
def trimr(mini, maxi, x):
np.clip(x, mini, maxi, out=x)
return x
def update_population(x, f):
row, col = x.shape
best_index = np.argmin(f)
worst_index = np.argmax(f)
Best = x[best_index, :]
worst = x[worst_index, :]
xnew = np.zeros((row, col))
for i in range(row):
for j in range(col):
xnew[i, j] = x[i, j] + np.random.rand() * (Best[j] - worst[j])
return xnew
def objective(x):
r, _ = x.shape
Z = np.zeros(r)
for i in range(r):
x1, x2 = x[i, 0], x[i, 1]
z = ((x1**2 + x2 - 11)**2) + ((x1 + x2**2 - 7)**2)
g1 = 26 - ((x1 - 5)**2) - (x2**2)
g2 = 20 - (4 * x1) - x2
p1 = 10 * (min(0, g1)**2) # penalty if constraint 1 is violated
p2 = 10 * (min(0, g2)**2) # penalty if constraint 2 is violated
Z[i] = z + p1 + p2 # penalized objective function value
return Z
# Run the Rao-1 algorithm
Rao_1()