Space-filling Latin hypercube designs based on randomization restrictions in factorial experiments

A Novel Latin Hypercube Algorithm via Translational Propagation

The Scientific World JOURNAL ◽

10.1155/2014/163949 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 5

Author(s):

Guang Pan ◽

Pengcheng Ye ◽

Peng Wang

Keyword(s):

Computation Time ◽

Latin Hypercube Design ◽

Latin Hypercube ◽

Space Filling ◽

Enumeration Algorithm ◽

Latin Hypercube Designs ◽

Initial Block ◽

Optimal Latin Hypercube Design ◽

Computationally Expensive ◽

Formal Optimization

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the experimental designs used. Optimal Latin hypercube designs are frequently used and have been shown to have good space-filling and projective properties. However, the high cost in constructing them limits their use. In this paper, a methodology for creating novel Latin hypercube designs via translational propagation and successive local enumeration algorithm (TPSLE) is developed without using formal optimization. TPSLE algorithm is based on the inspiration that a near optimal Latin Hypercube design can be constructed by a simple initial block with a few points generated by algorithm SLE as a building block. In fact, TPSLE algorithm offers a balanced trade-off between the efficiency and sampling performance. The proposed algorithm is compared to two existing algorithms and is found to be much more efficient in terms of the computation time and has acceptable space-filling and projective properties.

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Space-filling Latin hypercube designs for computer experiments

Optimization and Engineering ◽

10.1007/s11081-010-9129-8 ◽

2010 ◽

Vol 12 (4) ◽

pp. 611-630 ◽

Cited By ~ 66

Author(s):

Bart G. M. Husslage ◽

Gijs Rennen ◽

Edwin R. van Dam ◽

Dick den Hertog

Keyword(s):

Computer Experiments ◽

Latin Hypercube ◽

Space Filling ◽

Latin Hypercube Designs

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Optimal Sliced Latin Hypercube Designs with Slices of Arbitrary Run Sizes

Mathematics ◽

10.3390/math7090854 ◽

2019 ◽

Vol 7 (9) ◽

pp. 854 ◽

Cited By ~ 1

Author(s):

Jing Zhang ◽

Jin Xu ◽

Kai Jia ◽

Yimin Yin ◽

Zhengming Wang

Keyword(s):

Computer Experiments ◽

New Method ◽

Latin Hypercube ◽

Space Filling ◽

Latin Hypercube Designs ◽

Qualitative Factors ◽

Experimental Region

Sliced Latin hypercube designs (SLHDs) are widely used in computer experiments with both quantitative and qualitative factors and in batches. Optimal SLHDs achieve better space-filling property on the whole experimental region. However, most existing methods for constructing optimal SLHDs have restriction on the run sizes. In this paper, we propose a new method for constructing SLHDs with arbitrary run sizes, and a new combined space-filling measurement describing the space-filling property for both the whole design and its slices. Furthermore, we develop general algorithms to search for the optimal SLHD with arbitrary run sizes under the proposed measurement. Examples are presented to illustrate that effectiveness of the proposed methods.

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