We compared the performance of our method with several existing methods: RMs, support vector machine (SVM), NNs, NSGAII and SPEA2, which are two highly competitive algorithms for bi-objective optimization. We adopted 4 most commonly used RMs: multiple curvilinear regression with the kernel function of y = axb (RM1), y=1a+be−x (RM2), y = a+bx+cx2 (RM3), and y = a+bx+cx2+dx3 (RM4). Three kinds of NNs were adopted: back propagation NN (BPNN), general regression NN (GRNN), and radial basis function (RBF) NN. The reasons for selecting these NNs to present a contrast is that: BPNN is the most widely used NN, GRNN has the ability to converge to the underlying functions of the data with only few training samples available and needs relatively small knowledge to turn the parameter, and RBFNN has the characteristics of best approximation and global optimal performance.