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import torch
import numpy as np
#
#
# HeatExchanger: 8D objective, 6 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 HeatExchanger(individuals):
assert torch.is_tensor(individuals) and individuals.size(1) == 8, "Input must be an n-by-8 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,:]
x1 = x[0]
x2 = x[1]
x3 = x[2]
x4 = x[3]
x5 = x[4]
x6 = x[5]
x7 = x[6]
x8 = x[7]
## Negative sign to make it a maximization problem
test_function = - ( x1+x2+x3 )
fx.append(test_function)
## Calculate constraints terms
g1 = 0.0025 * (x4+x6) - 1
g2 = 0.0025 * (x5 + x7 - x4) - 1
g3 = 0.01 *(x8-x5) - 1
g4 = 833.33252*x4 + 100*x1 - x1*x6 - 83333.333
g5 = 1250*x5 + x2*x4 - x2*x7 - 125*x4
g6 = x3*x5 - 2500*x5 - x3*x8 + 125*10000
gx1.append( g1 )
gx2.append( g2 )
gx3.append( g3 )
gx4.append( g4 )
gx5.append( g5 )
gx6.append( g6 )
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))
gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6), 1)
return gx, fx
def HeatExchanger_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 8, "Input must be an n-by-8 PyTorch tensor."
x1 = (X[:,0] * (10000-100) + 100).reshape(X.shape[0],1)
x2 = (X[:,1] * (10000-1000) + 1000).reshape(X.shape[0],1)
x3 = (X[:,2] * (10000-1000) + 1000).reshape(X.shape[0],1)
x4 = (X[:,3] * (1000-10) + 10).reshape(X.shape[0],1)
x5 = (X[:,4] * (1000-10) + 10).reshape(X.shape[0],1)
x6 = (X[:,5] * (1000-10) + 10).reshape(X.shape[0],1)
x7 = (X[:,6] * (1000-10) + 10).reshape(X.shape[0],1)
x8 = (X[:,7] * (1000-10) + 10).reshape(X.shape[0],1)
X_scaled = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8), dim=1)
return X_scaled
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