期刊
CHINESE JOURNAL OF MECHANICAL ENGINEERING
卷 27, 期 6, 页码 1099-1111出版社
SPRINGEROPEN
DOI: 10.3901/CJME.2014.0820.138
关键词
surrogate-based optimization; global optimization; significant sampling space; adaptive surrogate; radial basis function
资金
- National Natural Science Foundation of China [51105040, 11372036]
- Aeronautical Science Foundation of China [2011ZA72003, 2009ZA72002]
- Excellent Young Scholars Research Fund of Beijing Institute of Technology [2010Y0102]
- Foundation Research Fund of Beijing Institute of Technology [20130142008]
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.
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