Spaces:
Sleeping
Sleeping
| import torch | |
| import numpy as np | |
| # | |
| # | |
| # CompressionSpring: 8D objective, 6 constraints | |
| # | |
| # Reference: | |
| # Gandomi AH, Yang XS, Alavi AH (2011) Mixed | |
| # variable structural optimization using firefly | |
| # algorithm. Computers & Structures 89(23- | |
| # 24):2325–2336 | |
| # | |
| # | |
| def CompressionSpring(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,:] | |
| # print(x) | |
| d = x[0] | |
| D = x[1] | |
| N = x[2] | |
| ## Negative sign to make it a maximization problem | |
| test_function = - ( (N+2)*D*d**2 ) | |
| fx.append(test_function) | |
| ## Calculate constraints terms | |
| g1 = 1 - ( D*D*D * N / (71785* d*d*d*d) ) | |
| g2 = (4*D*D - D*d) / (12566 * (D*d*d*d - d*d*d*d)) + 1/(5108*d*d) - 1 | |
| g3 = 1 - 140.45*d / (D*D * N) | |
| g4 = (D+d)/1.5 - 1 | |
| gx1.append( g1 ) | |
| gx2.append( g2 ) | |
| gx3.append( g3 ) | |
| gx4.append( g4 ) | |
| 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)) | |
| gx = torch.cat((gx1, gx2, gx3, gx4), 1) | |
| return gx, fx | |
| def CompressionSpring_Scaling(X): | |
| assert torch.is_tensor(X) and X.size(1) == 3, "Input must be an n-by-3 PyTorch tensor." | |
| d = (X[:,0] * ( 1 - 0.05 ) + 0.05 ).reshape(X.shape[0],1) | |
| D = (X[:,1] * ( 1.3 - 0.25 ) + 0.25 ).reshape(X.shape[0],1) | |
| N = (X[:,2] * ( 15 - 2 ) + 2 ).reshape(X.shape[0],1) | |
| X_scaled = torch.cat((d, D, N), dim=1) | |
| return X_scaled | |