Time Consuming Numerical Model Calibration Using Genetic Algorithm (GA), 1-Nearest Neighbor (1NN) Classifier and Principal Component Analysis (PCA).
Single Objective Genetic Algorithm (SGA) optimization process usually needs a large number of objective function evaluations before converging towards global optimum or a near-optimum. The SGA is used as automatic calibration method for a wide range of numerical models. However, the evaluation of the quality of solutions is very time-consuming in many real-world numerical model calibration problems. The algorithm SGA-1NN-PCA, an effective and efficient dynamic approximation model to reduce the number of actual fitness evaluations, is presented in this paper. Training data of 1NN classifier are produced from early generations. 1-Nearest Neighbor (1NN) classifier is used to predict objective function values for evaluations. Principal Component Analysis (PCA) linearly transforms high-dimensional optimization parameters into low-dimensional optimization parameters to save test time for 1NN. The test results show that the proposed method only requires about 25 percent of actual fitness evaluations of the SGA.
Liu
Yang
Y
Department of Engineering, Exeter University, EX4 4QF, UK.
Ye
Wen-Jing
WJ