Surrogate-based optimization for autonomous underwater vehicle's shell design

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

According to the AUVs' mission requirement of large operational depth, this paper provides a kind of ring-stiffened multiple intersecting spherical shell (RSMISS) for AUVs. In order to get the performance of the new shell, a surrogate-based optimization (SBO) method is proposed. The SBO method is employed to explore the kriging-based model. Simultaneously, a multi-start optimization approach is repeat-ed to do a global search. Furthermore, an enhanced Minimizing the Predictor (EMP) method is presented as the local infill criteria which can dynamically adjust the search direction and the length of sampling radius. Ultimately, the proposed SBO method is applied to look for the agreeable solution of the RSMISS. The results show that EMP can find the satisfactory solution more quickly with smaller computational cost.

Original languageEnglish
Title of host publicationProceedings - 14th International Symposium on Distributed Computing and Applications for Business, Engineering and Science, DCABES 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages393-397
Number of pages5
ISBN (Electronic)9781467365932
DOIs
StatePublished - 8 Mar 2016
Event14th International Symposium on Distributed Computing and Applications for Business, Engineering and Science, DCABES 2015 - Guiyang, China
Duration: 18 Aug 201524 Aug 2015

Publication series

NameProceedings - 14th International Symposium on Distributed Computing and Applications for Business, Engineering and Science, DCABES 2015

Conference

Conference14th International Symposium on Distributed Computing and Applications for Business, Engineering and Science, DCABES 2015
Country/TerritoryChina
CityGuiyang
Period18/08/1524/08/15

Keywords

  • Autonomous underwater vehicle
  • Kriging
  • Minimizing the Predictor
  • Multiple intersecting spherical shell
  • Surrogate-based optimization

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