An efficient multi-objective optimization method for black-box functions using sequential approximate technique

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• 作者：Guodong Chen ; Xu Han  ; ;   ; ^{hanxu@hnu.edu.cn} ;   ; ^{xu_han688@hotmail.com} ; Guiping Liu ; Chao Jiang ; Ziheng Zhao
• 关键词：Multi-objective optimization ; Black-box functions ; Sequential approximate technique ; Radial basis function ; Trust region ; Structure optimization
• 刊名：Applied Soft Computing Journal
• 出版年：2012
• 出版时间：January 2012
• 年：2012
• 卷：12
• 期：1
• 页码：14-27
• 全文大小：2521 K

文摘

Multi-objective optimization problems in practical engineering usually involve expensive black-box functions. How to reduce the number of function evaluations at a good approximation of Pareto frontier has been a crucial issue. To this aim, an efficient multi-objective optimization method based on a sequential approximate technique is suggested in this paper. In each iteration, according to the prediction of radial basis function with a micro multi-objective genetic algorithm, an extended trust region updating strategy is adopted to adjust the design region, a sample inheriting strategy is presented to reduce the number of new function evaluations, and then a local-densifying strategy is proposed to improve the accuracy of approximations in concerned regions. At the end of each iteration, the obtained actual Pareto optimal points are stored in an external archive and are updated as the iteration process. The effect of the present method is demonstrated by eight test functions. Finally, it is employed to perform the structure optimization of a vehicle door.