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基于元模型与聚类算法的设计空间减缩策略及工程应用
引用本文:周仕明,李道奎,唐国金.基于元模型与聚类算法的设计空间减缩策略及工程应用[J].计算力学学报,2012,29(2):242-248.
作者姓名:周仕明  李道奎  唐国金
作者单位:1. 国防科学技术大学指挥军官基础教育学院,长沙410073/国防科学技术大学航天与材料工程学院,长沙410073
2. 国防科学技术大学航天与材料工程学院,长沙,410073
基金项目:国家自然科学基金(10902121)资助项目.
摘    要:提出了一种基于元模型建模和聚类算法的设计空间减缩策略,能直接将设计空间减小到相对小的子空间。建议的方法分为三步:首先,进行低精度元模型建模,其次重新设计样本点并基于元模型求解样本点函数值和导数,应用最短距离层次聚类算法确定样本点聚类数与聚类中心,采用模糊c均值算法完成设计空间划分,生成设计空间子空间;最后,求解子空间目标函数的均值,确定保留子空间,并在保留子空间中对函数进行优化,达到目标函数全局最优解。测试函数和工程算例表明,该方法能够有效减小设计空间。

关 键 词:元模型  代理模型  Kriging模型  聚类算法

Design space reduction based on the metamodeling and clustering method
ZHOU Shi-ming,LI Dao-kui and TANG Guo-jin.Design space reduction based on the metamodeling and clustering method[J].Chinese Journal of Computational Mechanics,2012,29(2):242-248.
Authors:ZHOU Shi-ming  LI Dao-kui and TANG Guo-jin
Institution:School of Basic Education for Commanding Officers, National University of Defense Technology, Changsha 410073, China;1. School of Basic Education for Commanding Officers, National University of Defense Technology, Changsha 410073, China;School of Aerospace and Material Engineering, National University of Defense Technology, Changsha 410073, China
Abstract:A design space reduction methodology was proposed, which based on metamodeling and clustering method to reduce the design space to a relatively small region. This methodology is composed of three main steps. In the first step, the metamodel is constructed according to the initial samples. In the second step, new samples are generated, the function and derivative of the sample are computed based on the metamodel. The central and number of cluster is determined by the Nearest Neighbor method. The new samples are partitioned into several clusters by Fuzzy C-mean method and design subspaces are generated. Finally, several subspace filters out due to the function mean of every subspace. The global optimization result of objective function is obtained from the residual design subspaces. The test problem and engineering instance show the accuracy and efficiency of proposed method.
Keywords:metamodel  surrogate model  Kriging model  Clustering method
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