Multi-objective optimization based on meta-modeling by using support vector regression |
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Authors: | Yeboon Yun Min Yoon Hirotaka Nakayama |
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Affiliation: | (1) Faculty of Engineering, Kagawa University, 2217-20 Hayashicho, Takamatsu, Kagawa 761-0396, Japan;(2) Konkuk University, 134 Shinchon-Dong, Seodaemun-Gu, Seoul, 120-749, Republic of Korea;(3) Konan University, 8-9-1 Okamoto, Higashinada, Kobe 658-8501, Japan |
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Abstract: | Practical engineering design problems have a black-box objective function whose forms are not explicitly known in terms of design variables. In those problems, it is very important to make the number of function evaluations as few as possible in finding an optimal solution. So, in this paper, we propose a multi-objective optimization method based on meta-modeling predicting a form of each objective function by using support vector regression. In addition, we discuss a way how to select additional experimental data for sequentially revising a form of objective function. Finally, we illustrate the effectiveness of the proposed method through some numerical examples. |
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Keywords: | Multi-objective optimization Pareto frontier Support vector regression Sequential approximation method Evolutionary multi-objective optimization |
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