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import torch
import numpy as np
#
#
# SpeedReducer: 7D objective, 9 constraints
#
# Reference:
# Yang XS, Hossein Gandomi A (2012) Bat algo-
# rithm: a novel approach for global engineer-
# ing optimization. Engineering computations
# 29(5):464–483
#
#
def SpeedReducer(individuals):
assert torch.is_tensor(individuals) and individuals.size(1) == 7, "Input must be an n-by-7 PyTorch tensor."
fx = []
gx1 = []
gx2 = []
gx3 = []
gx4 = []
gx5 = []
gx6 = []
gx7 = []
gx8 = []
gx9 = []
gx10 = []
gx11 = []
n = individuals.size(0)
for i in range(n):
x = individuals[i,:]
b = x[0]
m = x[1]
z = x[2]
L1 = x[3]
L2 = x[4]
d1 = x[5]
d2 = x[6]
C1 = 0.7854*b*m*m
C2 = 3.3333*z*z + 14.9334*z - 43.0934
C3 = 1.508*b*(d1*d1 + d2*d2)
C4 = 7.4777*(d1*d1*d1 + d2*d2*d2)
C5 = 0.7854*(L1*d1*d1 + L2*d2*d2)
## Negative sign to make it a maximization problem
test_function = - ( 0.7854*b*m*m * (3.3333*z*z + 14.9334*z - 43.0934) - 1.508*b*(d1*d1 + d2*d2) + 7.4777*(d1*d1*d1 + d2*d2*d2) + 0.7854*(L1*d1*d1 + L2*d2*d2) )
fx.append(test_function)
## Calculate constraints terms
g1 = 27/(b*m*m*z) - 1
g2 = 397.5/(b*m*m*z*z) - 1
g3 = 1.93*L1**3 /(m*z *d1**4) - 1
g4 = 1.93*L2**3 /(m*z *d2**4) - 1
g5 = np.sqrt( (745*L1/(m*z))**2 + 1.69*1e6 ) / (110*d1**3) -1
g6 = np.sqrt( (745*L2/(m*z))**2 + 157.5*1e6 ) / (85*d2**3) -1
g7 = m*z/40 - 1
g8 = 5*m/(b) - 1
g9 = b/(12*m) -1
gx1.append( g1 )
gx2.append( g2 )
gx3.append( g3 )
gx4.append( g4 )
gx5.append( g5 )
gx6.append( g6 )
gx7.append( g7 )
gx8.append( g8 )
gx9.append( g9 )
fx = torch.tensor(fx)
fx = torch.reshape(fx, (len(fx),1))
gx1 = torch.tensor(gx1)
gx1 = gx1.reshape((n, 1))
gx2 = torch.tensor(gx2)
gx2 = gx2.reshape((n, 1))
gx3 = torch.tensor(gx3)
gx3 = gx3.reshape((n, 1))
gx4 = torch.tensor(gx4)
gx4 = gx4.reshape((n, 1))
gx5 = torch.tensor(gx5)
gx5 = gx1.reshape((n, 1))
gx6 = torch.tensor(gx6)
gx6 = gx2.reshape((n, 1))
gx7 = torch.tensor(gx7)
gx7 = gx3.reshape((n, 1))
gx8 = torch.tensor(gx8)
gx8 = gx4.reshape((n, 1))
gx9 = torch.tensor(gx9)
gx9 = gx4.reshape((n, 1))
gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6, gx7, gx8, gx9), 1)
return gx, fx
def SpeedReducer_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 7, "Input must be an n-by-7 PyTorch tensor."
b = (X[:,0] * ( 3.6 - 2.6 ) + 2.6).reshape(X.shape[0],1)
m = (X[:,1] * ( 0.8 - 0.7 ) + 0.7).reshape(X.shape[0],1)
z = (X[:,2] * ( 28 - 17 ) + 17).reshape(X.shape[0],1)
L1 = (X[:,3] * ( 8.3 - 7.3 ) + 7.3).reshape(X.shape[0],1)
L2 = (X[:,4] * ( 8.3 - 7.3 ) + 7.3).reshape(X.shape[0],1)
d1 = (X[:,5] * ( 3.9 - 2.9 ) + 2.9).reshape(X.shape[0],1)
d2 = (X[:,6] * ( 5.5 - 5 ) + 5).reshape(X.shape[0],1)
X_scaled = torch.cat((b, m, z, L1, L2, d1, d2), dim=1)
return X_scaled
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