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

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

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...

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Veröffentlicht in:IEEE transactions on magnetics 2017-06, Vol.53 (6), p.1-4
Hauptverfasser: Yinjiang Li, Song Xiao, Rotaru, Mihai, Sykulski, Jan K.
Format: Artikel
Sprache:eng
Schlagworte:
Agglomeration
Algorithm design and analysis
Clustering
Computational modeling
Correlation
Datasets
Efficiency
kriging
Kriging interpolation
large data sets
Linear programming
Magnetism
Mathematical models
Optimization
Predictive models
surrogate optimization
Two dimensional displays
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Zusammenfassung: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.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2017.2665703