TY - GEN
T1 - A fast optimization method of using nondominated sorting genetic algorithm (NSGA-II) and 1-nearest neighbor (1NN) classifier for numerical model calibration
AU - Liu, Y.
AU - Zhou, C.
AU - Ye, W. J.
PY - 2005
Y1 - 2005
N2 - Practical experience with numerical model calibration suggests that no single objective is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. The multi-objective genetic algorithm (MOGA) is used as automatic calibration method for a wide range of numerical models. The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real-world engineering problems. The NSGA-II-1NN algorithm, an effective and efficient methodology to reduce the number of actual fitness evaluations for solving the multiple-objective global optimization problem, is presented in this paper. The test results for multi-objective calibration show that the proposed method only requires about 38 percent of actual fitness evaluations of the NSGA-II.
AB - Practical experience with numerical model calibration suggests that no single objective is adequate to measure the ways in which the model fails to match the important characteristics of the observed data. The multi-objective genetic algorithm (MOGA) is used as automatic calibration method for a wide range of numerical models. The task of estimating the entire Pareto set requires a large number of fitness evaluations in a standard MOGA optimization process. However, it's very time consuming to obtain a value of objective functions in many real-world engineering problems. The NSGA-II-1NN algorithm, an effective and efficient methodology to reduce the number of actual fitness evaluations for solving the multiple-objective global optimization problem, is presented in this paper. The test results for multi-objective calibration show that the proposed method only requires about 38 percent of actual fitness evaluations of the NSGA-II.
UR - http://www.scopus.com/inward/record.url?scp=33845311515&partnerID=8YFLogxK
U2 - 10.1109/GRC.2005.1547351
DO - 10.1109/GRC.2005.1547351
M3 - Conference Proceeding
AN - SCOPUS:33845311515
SN - 0780390172
SN - 9780780390171
T3 - 2005 IEEE International Conference on Granular Computing
SP - 544
EP - 549
BT - 2005 IEEE International Conference on Granular Computing
T2 - 2005 IEEE International Conference on Granular Computing
Y2 - 25 July 2005 through 27 July 2005
ER -