Spaces:
Sleeping
Sleeping
File size: 3,679 Bytes
165ee00 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 |
import torch
import numpy as np
#
#
# CantileverBeam: 10D objective, 11 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 CantileverBeam(individuals):
assert torch.is_tensor(individuals) and individuals.size(1) == 10, "Input must be an n-by-10 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]
x9 = x[8]
x10 = x[9]
P = 50000
E = 2*107
L = 100
## Negative sign to make it a maximization problem
test_function = - ( x1*x6*L + x2*x7*L + x3*x8*L + x4*x9*L + x5*x10*L )
fx.append(test_function)
## Calculate constraints terms
g1 = 600 * P / (x5*x10*x10) - 14000
g2 = 6 * P * (L*2) / (x4*x9*x9) - 14000
g3 = 6 * P * (L*3) / (x3*x8*x8) - 14000
g4 = 6 * P * (L*4) / (x2*x7*x7) - 14000
g5 = 6 * P * (L*5) / (x1*x6*x6) - 14000
g6 = P* L**3 * (1/L + 7/L + 19/L + 37/L + 61/L) / (3*E) -2.7
g7 = x10/x5 - 20
g8 = x9/x4 - 20
g9 = x8/x3 - 20
g10 = x7/x2 - 20
g11 = x6/x1 - 20
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 )
gx10.append( g10 )
gx11.append( g11 )
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))
gx10 = torch.tensor(gx10)
gx10 = gx4.reshape((n, 1))
gx11 = torch.tensor(gx11)
gx11 = gx4.reshape((n, 1))
gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6, gx7, gx8, gx9, gx10, gx11), 1)
return gx, fx
def CantileverBeam_Scaling(X):
assert torch.is_tensor(X) and X.size(1) == 10, "Input must be an n-by-10 PyTorch tensor."
x1 = (X[:,0] * (5-1) + 1).reshape(X.shape[0],1)
x2 = (X[:,1] * (5-1) + 1).reshape(X.shape[0],1)
x3 = (X[:,2] * (5-1) + 1).reshape(X.shape[0],1)
x4 = (X[:,3] * (5-1) + 1).reshape(X.shape[0],1)
x5 = (X[:,4] * (5-1) + 1).reshape(X.shape[0],1)
x6 = (X[:,5] * (65-30) + 30).reshape(X.shape[0],1)
x7 = (X[:,6] * (65-30) + 30).reshape(X.shape[0],1)
x8 = (X[:,7] * (65-30) + 30).reshape(X.shape[0],1)
x9 = (X[:,8] * (65-30) + 30).reshape(X.shape[0],1)
x10 = (X[:,9] * (65-30) + 30).reshape(X.shape[0],1)
X_scaled = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8, x9, x10), dim=1)
return X_scaled
|