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import torch
import numpy as np
#
#
# ReinforcedConcreteBeam: 3D objective, 9 constraints
#
# Reference:
# Gandomi AH, Yang XS, Alavi AH (2011) Mixed
# variable structural optimization using firefly
# algorithm. Computers & Structures 89(23-
# 24):2325–2336
#
#
def ReinforcedConcreteBeam(individuals):
assert torch.is_tensor(individuals) and individuals.size(1) == 3, "Input must be an n-by-3 PyTorch tensor."
fx = []
gx1 = []
gx2 = []
gx3 = []
gx4 = []
n = individuals.size(0)
for i in range(n):
x = individuals[i,:]
As = x[0]
h = x[1]
b = x[2]
test_function = - ( 29.4*As + 0.6*b*h )
fx.append(test_function)
g1 = h/b - 4
g2 = 180 + 7.35*As*As/b - As*h
gx1.append( g1 )
gx2.append( g2 )
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))
gx = torch.cat((gx1, gx2), 1)
return gx, fx
def ReinforcedConcreteBeam_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 3, "Input must be an n-by-3 PyTorch tensor."
As = (X[:,0] * (15-0.2) + 0.2).reshape(X.shape[0],1)
b = (X[:,1] * (40-28) +28).reshape(X.shape[0],1)
h = (X[:,2] * 5 + 5).reshape(X.shape[0],1)
X_scaled = torch.cat((As, b, h), dim=1)
return X_scaled
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