Spaces:
Sleeping
Sleeping
| 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 | |