Optimal Noncollapsing Space-Filling Designs for Irregular Experimental Regions

Ray Bing Chen, Chi Hao Li, Ying Hung, Weichung Wang

Research output: Contribution to journalArticlepeer-review


Space-filling and noncollapsing are two important properties in designing computer experiments. We study how the noncollapsing, space-filling designs for irregular experimental regions can be generated efficiently by the proposed metaheuristic methods. We solve this optimal design problem using variants of the discrete particle swarm optimization (DPSO) approaches. Numerical results, including an application in data center thermal management, are used to illustrate the performances of the proposed algorithms. Based on these numerical results, we assert that the most efficient approach is to reformulate the target optimal design problem as a constrained optimization problem and then use a modified DPSO to solve the constrained optimization problem.

Original languageEnglish
Pages (from-to)74-91
Number of pages18
JournalJournal of Computational and Graphical Statistics
Issue number1
Publication statusPublished - 2019 Jan 2

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Discrete Mathematics and Combinatorics
  • Statistics, Probability and Uncertainty

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