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An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing

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  • Nantiwat Pholdee
  • Sujin Bureerat

Abstract

This paper proposes a new optimal Latin hypercube sampling method (OLHS) for design of a computer experiment. The new method is based on solving sequencing and continuous optimisation using simulated annealing. There are two sets of design variables used in the optimisation process: sequencing and real number variables. The special mutation operator is developed to deal with such design variables. The performance of the proposed numerical strategy is tested and compared with three established OLHS methods, namely genetic algorithm (GA), enhanced stochastic evolutionary algorithm (ESEA) and successive local enumeration (SLE). Based on 30 test problems with various design dimensions and numbers of sampling points, the proposed method gives the best results. The method can generate an optimum set of sampling points within reasonable computing time; therefore, it can be considered as a powerful tool for design of computer experiments.

Suggested Citation

  • Nantiwat Pholdee & Sujin Bureerat, 2015. "An efficient optimum Latin hypercube sampling technique based on sequencing optimisation using simulated annealing," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(10), pages 1780-1789, July.
    • Handle: RePEc:taf:tsysxx:v:46:y:2015:i:10:p:1780-1789
    DOI: 10.1080/00207721.2013.835003
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    Cited by:

    1. Yajing Gao & Jing Zhu & Huaxin Cheng & Fushen Xue & Qing Xie & Peng Li, 2016. "Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories," Energies, MDPI, vol. 9(7), pages 1-15, July.
    2. Wu, Zeping & Wang, Wenjie & Wang, Donghui & Zhao, Kun & Zhang, Weihua, 2019. "Global sensitivity analysis using orthogonal augmented radial basis function," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 291-302.

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