Searching for optimal Latin hypercube designs by a local greedy strategy

Abstract

The Latin hypercube design (LHD), because of its one-dimensional projection uniformity, is commonly used in computer experiment. The randomly generated LHD may have too many concentrated design points, and factors may be highly correlated. In this article, we suggested a local greedy strategy for searching optimal LHDs. Our strategy consists of two parts. One is a swap process for doing a local greedy search in a polynomial time. The other is a simulated annealing process for jumping out of the possible local optima. Our strategy is flexible and adapts to various space-filling criteria of LHDs. The simulated experiments illustrated that our proposed algorithm can produce LHDs with well space-filling property and orthogonality. Compared to other classical design algorithms, our algorithm performed better on the criteria related to the point distance and the column correlation. Moreover, for the response surface approximation, the Kriging model using our produced optimal LHD performed more robust on the surface prediction.

Keywords:

• Computer experiment
• Latin hypercube design
• Simulated annealing

Notes

1 http://www.stat.rutgers.edu/home/yhung/OMLHD\%20C++\%20code.txt

2 https://CRAN.R-project.org/package=LHD

3 https://CRAN.R-project.org/package=MaxPro

Additional information

Funding

This work is supported by National Key R&D Program of China (No. 2020YFA0714102), and the National Natural Foundation of China (Nos. 12171076, 12226003).