Sequential Optimization Algorithm Based on Support Vector Regression
In order to improve the efficiency of engineering optimization problems, a variety of approximate modeling methods have been developed to express the complex model for reducing the computational expense, but because of the error between approximate model and actual model, optimization results obtained with approximate model are not always consist with that obtained with actual model. In this paper, we adopt support vector regression (SVR) as the new approximate modeling technology, and in order to ensure that approximate optimum solution is consist with actual optimum solution, sequential optimization algorithm is proposed, where the approximate model is persistently updated in the optimization process according to the optimization results, and the approximate optimum solution is gradually approach the actual optimum solution. A numerical example is used to test the proposed algorithm, and experimental results show that the improved sequential optimization algorithm based on SVR is effective to obtain the optimum solution consist with actual optimum solution.
support vector regression approximate modeling repository sequential optimization
Xixiang Yang Zhenyu Jiang Weihua Zhang
School of Aerospace and Material Engineering National University of Defense Technology Changsha, Hunan Province, China
国际会议
太原
英文
305-309
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）