2019/08/06 by Jing Zhang, Zhang, Jing, Jin Xu +7
Computer Science · Engineering · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Mathematics #Statistics Theory (math.ST) #VLSI and FPGA Design Techniques
paper · pdf · doi:10.48550/arxiv.1908.01976
openalex publication_date 2019/08/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 the optimal SLHD with arbitrary run sizes under the proposed measurement. Examples are presented to illustrate that effectiveness of the proposed methods.