Optimization Strategy Using Dynamic Metamodel Based on Trust Region and Biased Sampling Method |
| |
Authors: | Jianqiao Yu Fangzheng Chen Yuanchuan Shen |
| |
Affiliation: | School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China,School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China and School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China |
| |
Abstract: | Combining a trust region method with a biased sampling method, a novel optimization strategy (TR-BS-KRG) based on a dynamic metamodel is proposed. Initial sampling points are selected by a maximin Latin hypercube design method, and the metamodel is constructed with Kriging functions. The global optimization algorithm is employed to perform the biased sampling by searching the maximum expectation improvement point or the minimum of surrogate prediction point within the trust region. And the trust region is updated according to the current known information. The iteration continues until the potential global solution of the true optimization problem satisfied the convergence conditions. Compared with the trust region method and the biased sampling method, the proposed optimization strategy can obtain the global optimal solution to the test case, in which improvements in computation efficiency are also shown. When applied to an aerodynamic design optimization problem, the aerodynamic performance of tandem UAV is improved while meeting the constraints, which verifies its engineering application. |
| |
Keywords: | Kriging metamodel expected improvement trust region design optimization |
本文献已被 万方数据 等数据库收录! |
| 点击此处可从《北京理工大学学报(英文版)》浏览原始摘要信息 |
|
点击此处可从《北京理工大学学报(英文版)》下载全文 |
|