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| import torch | |
| import numpy as np | |
| # | |
| # | |
| # Car: 11D objective, 10 constraints | |
| # | |
| # Reference: | |
| # Gandomi AH, Yang XS, Alavi AH (2011) Mixed | |
| # variable structural optimization using firefly | |
| # algorithm. Computers & Structures 89(23- | |
| # 24):2325–2336 | |
| # | |
| # | |
| def Car(individuals): | |
| assert torch.is_tensor(individuals) and individuals.size(1) == 11, "Input must be an n-by-11 PyTorch tensor." | |
| n = individuals.size(0) | |
| fx = torch.zeros((n,1)) | |
| gx1 = torch.zeros((n,1)) | |
| gx2 = torch.zeros((n,1)) | |
| gx3 = torch.zeros((n,1)) | |
| gx4 = torch.zeros((n,1)) | |
| gx5 = torch.zeros((n,1)) | |
| gx6 = torch.zeros((n,1)) | |
| gx7 = torch.zeros((n,1)) | |
| gx8 = torch.zeros((n,1)) | |
| gx9 = torch.zeros((n,1)) | |
| gx10 = torch.zeros((n,1)) | |
| gx11 = torch.zeros((n,1)) | |
| n = individuals.size(0) | |
| # Set function and constraints here: | |
| 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] | |
| x11 = x[10] | |
| ## Negative sign to make it a maximization problem | |
| test_function = - ( 1.98 + 4.90*x1 + 6.67*x2 + 6.98*x3 + 4.01*x4 + 1.78*x5 + 2.73*x7 ) | |
| ## Calculate constraints terms | |
| g1 = 1.16 - 0.3717*x2*x4 - 0.00931*x2*x10 - 0.484*x3*x9 + 0.01343*x6*x10 -1 | |
| g2 = (0.261 - 0.0159*x1*x2 - 0.188*x1*x8 | |
| - 0.019*x2*x7 + 0.0144*x3*x5 + 0.0008757*x5*x10 | |
| + 0.08045*x6*x9 + 0.00139*x8*x11 + 0.00001575*x10*x11) -0.9 | |
| g3 = (0.214 + 0.00817*x5 - 0.131*x1*x8 - 0.0704*x1*x9 + 0.03099*x2*x6 | |
| -0.018*x2*x7 + 0.0208*x3*x8 + 0.121*x3*x9 - 0.00364*x5*x6 | |
| +0.0007715*x5*x10 - 0.0005354*x6*x10 + 0.00121*x8*x11) -0.9 | |
| g4 = 0.74 -0.061*x2 -0.163*x3*x8 +0.001232*x3*x10 -0.166*x7*x9 +0.227*x2*x2 -0.9 | |
| g5 = 28.98 +3.818*x3-4.2*x1*x2+0.0207*x5*x10+6.63*x6*x9-7.7*x7*x8+0.32*x9*x10 -32 | |
| g6 = 33.86 +2.95*x3+0.1792*x10-5.057*x1*x2-11.0*x2*x8-0.0215*x5*x10-9.98*x7*x8+22.0*x8*x9 -32 | |
| g7 = 46.36 -9.9*x2-12.9*x1*x8+0.1107*x3*x10 -32 | |
| g8 = 4.72 -0.5*x4-0.19*x2*x3-0.0122*x4*x10+0.009325*x6*x10+0.000191*x11**2 -4 | |
| g9 = 10.58 -0.674*x1*x2-1.95*x2*x8+0.02054*x3*x10-0.0198*x4*x10+0.028*x6*x10 -9.9 | |
| g10 = 16.45 -0.489*x3*x7-0.843*x5*x6+0.0432*x9*x10-0.0556*x9*x11-0.000786*x11**2 -15.7 | |
| gx1[i] = g1 | |
| gx2[i] = g2 | |
| gx3[i] = g3 | |
| gx4[i] = g4 | |
| gx5[i] = g5 | |
| gx6[i] = g6 | |
| gx7[i] = g7 | |
| gx8[i] = g8 | |
| gx9[i] = g9 | |
| gx10[i] = g10 | |
| fx[i] = test_function | |
| gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6, gx7, gx8, gx9, gx10), 1) | |
| return gx, fx | |
| def Car_Scaling(X): | |
| assert torch.is_tensor(X) and X.size(1) == 11, "Input must be an n-by-11 PyTorch tensor." | |
| x1 = (X[:,0] * (1.5-0.5) + 0.5).reshape(X.shape[0],1) | |
| x2 = (X[:,1] * (1.35-0.45) + 0.45).reshape(X.shape[0],1) | |
| x3 = (X[:,2] * (1.5-0.5) + 0.5).reshape(X.shape[0],1) | |
| x4 = (X[:,3] * (1.5-0.5) + 0.5).reshape(X.shape[0],1) | |
| x5 = (X[:,4] * (1.5-0.5) + 0.5).reshape(X.shape[0],1) | |
| x6 = (X[:,5] * (1.5-0.5) + 0.5).reshape(X.shape[0],1) | |
| x7 = (X[:,6] * (1.5-0.5) + 0.5).reshape(X.shape[0],1) | |
| x8 = (X[:,7] * (0.345-0.192) + 0.192).reshape(X.shape[0],1) | |
| x9 = (X[:,8] * (0.345-0.192) + 0.192).reshape(X.shape[0],1) | |
| x10 = (X[:,9] * (-20)).reshape(X.shape[0],1) | |
| x11 = (X[:,10] * (-20)).reshape(X.shape[0],1) | |
| X_scaled = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11), dim=1) | |
| return X_scaled | |
| def Car_softpen(individuals): | |
| assert torch.is_tensor(individuals) and individuals.size(1) == 11, "Input must be an n-by-11 PyTorch tensor." | |
| n = individuals.size(0) | |
| fx = torch.zeros((n,1)) | |
| gx1 = torch.zeros((n,1)) | |
| gx2 = torch.zeros((n,1)) | |
| gx3 = torch.zeros((n,1)) | |
| gx4 = torch.zeros((n,1)) | |
| gx5 = torch.zeros((n,1)) | |
| gx6 = torch.zeros((n,1)) | |
| gx7 = torch.zeros((n,1)) | |
| gx8 = torch.zeros((n,1)) | |
| gx9 = torch.zeros((n,1)) | |
| gx10 = torch.zeros((n,1)) | |
| gx11 = torch.zeros((n,1)) | |
| n = individuals.size(0) | |
| # Set function and constraints here: | |
| 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] | |
| x11 = x[10] | |
| ## Negative sign to make it a maximization problem | |
| test_function = - ( 1.98 + 4.90*x1 + 6.67*x2 + 6.98*x3 + 4.01*x4 + 1.78*x5 + 2.73*x7 ) | |
| ## Calculate constraints terms | |
| g1 = 1.16 - 0.3717*x2*x4 - 0.00931*x2*x10 - 0.484*x3*x9 + 0.01343*x6*x10 -1 | |
| g2 = (0.261 - 0.0159*x1*x2 - 0.188*x1*x8 | |
| - 0.019*x2*x7 + 0.0144*x3*x5 + 0.0008757*x5*x10 | |
| + 0.08045*x6*x9 + 0.00139*x8*x11 + 0.00001575*x10*x11) -0.9 | |
| g3 = (0.214 + 0.00817*x5 - 0.131*x1*x8 - 0.0704*x1*x9 + 0.03099*x2*x6 | |
| -0.018*x2*x7 + 0.0208*x3*x8 + 0.121*x3*x9 - 0.00364*x5*x6 | |
| +0.0007715*x5*x10 - 0.0005354*x6*x10 + 0.00121*x8*x11) -0.9 | |
| g4 = 0.74 -0.061*x2 -0.163*x3*x8 +0.001232*x3*x10 -0.166*x7*x9 +0.227*x2*x2 -0.9 | |
| g5 = 28.98 +3.818*x3-4.2*x1*x2+0.0207*x5*x10+6.63*x6*x9-7.7*x7*x8+0.32*x9*x10 -32 | |
| g6 = 33.86 +2.95*x3+0.1792*x10-5.057*x1*x2-11.0*x2*x8-0.0215*x5*x10-9.98*x7*x8+22.0*x8*x9 -32 | |
| g7 = 46.36 -9.9*x2-12.9*x1*x8+0.1107*x3*x10 -32 | |
| g8 = 4.72 -0.5*x4-0.19*x2*x3-0.0122*x4*x10+0.009325*x6*x10+0.000191*x11**2 -4 | |
| g9 = 10.58 -0.674*x1*x2-1.95*x2*x8+0.02054*x3*x10-0.0198*x4*x10+0.028*x6*x10 -9.9 | |
| g10 = 16.45 -0.489*x3*x7-0.843*x5*x6+0.0432*x9*x10-0.0556*x9*x11-0.000786*x11**2 -15.7 | |
| gx1[i] = g1 | |
| gx2[i] = g2 | |
| gx3[i] = g3 | |
| gx4[i] = g4 | |
| gx5[i] = g5 | |
| gx6[i] = g6 | |
| gx7[i] = g7 | |
| gx8[i] = g8 | |
| gx9[i] = g9 | |
| gx10[i] = g10 | |
| fx[i] = test_function | |
| gx = torch.cat((gx1, gx2, gx3, gx4, gx5, gx6, gx7, gx8, gx9, gx10), 1) | |
| cost = gx | |
| cost[cost<0] = 0 | |
| cost = cost.sum(dim=1).reshape(cost.shape[0], 1) | |
| fx = fx + cost | |
| return gx, fx | |