Extended Multipoint Approximation Method. In: DEStech Transactions on Computer Science and Engineering.

Cheng-Yang Liu, Dianzi Liu, Xiaoan Mao, Xue Zhou

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

Stemming from polynomial metamodels, multipoint approximation method (MAM) and moving least square method (MLSM) focus on the development of metamodels for the objective and constraint functions in solving a mid-range optimization problem with a trust region. Although both of these methods could solve problems successfully, there is still some room for improvement on the computational effort and search capability. To address this problem, the extended multipoint approximation method is proposed to seek the optimal solution in this paper. The developed method assimilating the advantage of Taylor’s expansion used in MLSM demonstrates its superiority over other methods in terms of the computational efficiency and accuracy by some well-established benchmark problems.
Original languageEnglish
Title of host publication2nd International Conference on Applied Mathematics, Simulation and Modelling
Place of PublicationPhuket, Thailand
PublisherDestech Publications Inc.
Pages219-225
Number of pages6
ISBN (Print)978-1-60595-480-6
DOIs
Publication statusPublished - 2018
Event2nd International Conference on Applied Mathematics, Simulation and Modelling - Phuket, Thailand
Duration: 6 Aug 20177 Aug 2017

Conference

Conference2nd International Conference on Applied Mathematics, Simulation and Modelling
Abbreviated titleAMSM 2017
CountryThailand
CityPhuket
Period6/08/177/08/17

Keywords

  • Metamodel
  • Multipoint approximation method
  • Moving least square method
  • Taylor’s expansion

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    Liu, C-Y., Liu, D., Mao, X., & Zhou, X. (2018). Extended Multipoint Approximation Method. In: DEStech Transactions on Computer Science and Engineering. In 2nd International Conference on Applied Mathematics, Simulation and Modelling (pp. 219-225). Phuket, Thailand: Destech Publications Inc.. https://doi.org/10.12783/dtetr/amsm2017/14847