科创中国

原文链接：万方

• 作者：

  PENG Lei,LIU Li,LONG Teng,GUO Xiaosong

• 摘要：

  As a promising technique, surrogate-based design and optimization(SBDO) has been widely used in modern engineering design optimizations. Currently, static surrogate-based optimization methods have been successfully applied to expensive optimization problems. However, due to the low efficiency and poor flexibility, static surrogate-based optimization methods are difficult to efficiently solve practical engineering cases. At the aim of enhancing efficiency, a novel surrogate-based efficient optimization method is developed by using sequential radial basis function(SEO-SRBF). Moreover, augmented Lagrangian multiplier method is adopted to solve the problems involving expensive constraints. In order to study the performance of SEO-SRBF, several numerical benchmark functions and engineering problems are solved by SEO-SRBF and other well-known surrogate-based optimization methods including EGO, MPS, and IARSM. The optimal solutions, number of function evaluations, and algorithm execution time are recorded for comparison. The comparison results demonstrate that SEO-SRBF shows satisfactory performance in both optimization efficiency and global convergence capability. The CPU time required for running SEO-SRBF is dramatically less than that of other algorithms. In the torque arm optimization case using FEA simulation, SEO-SRBF further reduces 21% of the material volume compared with the solution from static-RBF subject to the stress constraint. This study provides the efficient strategy to solve expensive constrained optimization problems.

• 关键词：

  surrogate-based optimization global optimization significant sampling space adaptive surrogate radial basis function

• 作者单位：

  Key Laboratory of Dynamics and Control of Flight Vehicle,Ministry of Education%School of Aerospace Engineering,Beijing Institute of Technology

• 基金项目：

  Supported by National Natural Science Foundation of China (Grant Nos.51105040,11372036)%Aeronautical Science Foundation of China (Grant Nos.2011ZA72003,2009ZA72002)%Excellent Young Scholars Research Fund of Beijing Institute of Technology (Grant No.2010Y0102)%Foundation Research Fund of Beijing Institute of Technology (Grant No.20130142008)

• 来源期刊：

  中国机械工程学报(英文版)

• 年，卷(期)：

  2014006