A Kriging-Based Optimization Approach for Large Data Sets Exploiting Points Aggregation Techniques

被引：2

作者：

Li, Yinjiang ^{[1
]}

Xiao, Song ^{[1
]}

Rotaru, Mihai ^{[1
]}

Sykulski, Jan K. ^{[1
]}

机构：

[1] Univ Southampton, Elect & Comp Sci, Southampton SO17 1BJ, Hants, England

来源：

IEEE TRANSACTIONS ON MAGNETICS | 2017年 / 53卷 / 06期

关键词：

Clustering; kriging; large data sets; surrogate optimization;

D O I：

10.1109/TMAG.2017.2665703

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A kriging-based optimization approach is proposed for problems with large data sets and high dimensionality. Memory usage is maintained via model centering aided by minimizing the impact of information loss on accuracy of new point prediction using points aggregation techniques. The eight-parameter TEAM problem 22 is revisited in the context of computational efficiency and accuracy.

引用

页数：4

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