TY - JOUR
T1 - Optimization strategy using dynamic radial basis function metamodel based on trust region
AU - Long, Teng
AU - Guo, Xiaosong
AU - Peng, Lei
AU - Liu, Li
PY - 2014/4/5
Y1 - 2014/4/5
N2 - To improve the design quality and optimization efficiency of complicated engineering systems such as flight vehicle, metamodel based optimization is applied widely. Trust region is imported into the metamodel optimization and the updating strategy for sampling space using trust region is proposed and then the optimization strategy using dynamic radial basis function metamodel based on trust region is proposed. The metamodel is constructed with radial basis function and initial sampling points selected by Maximin latin hypercube design method. Global optimization algorithm is employed to optimize current metamodel to find the potential global optimum of the true optimization problem. According to the current known information, the trust region sampling space is updated. During optimization process, the new sampling points in the trust region sampling space are added, and the metamodel is updated, until the potential global optimum of the true optimization problem is satisfied the convergence conditions. The optimization strategy is validated by using five benchmark numerical test problems and an I-beam design problem. As the optimization results shown, the capability of TR-DRBF in both aspects of optimization efficiency and global convergency is good compared with the study fruit inland and overseas at present. Especially for high dimension problems, the performance of TR-DRBF is appealing.
AB - To improve the design quality and optimization efficiency of complicated engineering systems such as flight vehicle, metamodel based optimization is applied widely. Trust region is imported into the metamodel optimization and the updating strategy for sampling space using trust region is proposed and then the optimization strategy using dynamic radial basis function metamodel based on trust region is proposed. The metamodel is constructed with radial basis function and initial sampling points selected by Maximin latin hypercube design method. Global optimization algorithm is employed to optimize current metamodel to find the potential global optimum of the true optimization problem. According to the current known information, the trust region sampling space is updated. During optimization process, the new sampling points in the trust region sampling space are added, and the metamodel is updated, until the potential global optimum of the true optimization problem is satisfied the convergence conditions. The optimization strategy is validated by using five benchmark numerical test problems and an I-beam design problem. As the optimization results shown, the capability of TR-DRBF in both aspects of optimization efficiency and global convergency is good compared with the study fruit inland and overseas at present. Especially for high dimension problems, the performance of TR-DRBF is appealing.
KW - Dynamic metamodel
KW - Multidisciplinary design optimization
KW - Radial basis function
KW - Trust region
UR - http://www.scopus.com/inward/record.url?scp=84901244733&partnerID=8YFLogxK
U2 - 10.3901/JME.2014.07.184
DO - 10.3901/JME.2014.07.184
M3 - Article
AN - SCOPUS:84901244733
SN - 0577-6686
VL - 50
SP - 184
EP - 190
JO - Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
JF - Jixie Gongcheng Xuebao/Chinese Journal of Mechanical Engineering
IS - 7
ER -