Bayesian methods for multi-objective optimization of a supersonic wing planform

Timothy Man Shui Jim, Ghifari Adam Faza, Pramudita Satria Palar, Koji Shimoyama

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

Abstract

The global design1 of a supersonic wing planform is demonstrated using gradient-based and gradient-free surrogate-assisted Bayesian optimization utilizing expected hypervolume improvement as the optimization metric. The planform is parameterized using 6- and 11-variables. Representative of a simple supersonic business-jet conceptual-level design, the wing-body is optimized for low inviscid drag and low A-weighted ground-level noise. The speed of convergence to the non-dominated front and suitability of the two optimization implementations are compared, and the advantages over using a genetic algorithm directly are observed. A novel method for initial candidate sample generation, effective non-dominated from sampling, is proposed to further accelerate the convergence of samples towards Pareto solutions.

Original languageEnglish
Title of host publicationGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
PublisherAssociation for Computing Machinery, Inc
Pages1641-1643
Number of pages3
ISBN (Electronic)9781450371278
DOIs
Publication statusPublished - 2020 Jul 8
Event2020 Genetic and Evolutionary Computation Conference, GECCO 2020 - Cancun, Mexico
Duration: 2020 Jul 82020 Jul 12

Publication series

NameGECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

Conference

Conference2020 Genetic and Evolutionary Computation Conference, GECCO 2020
CountryMexico
CityCancun
Period20/7/820/7/12

Keywords

  • Bayesian optimization
  • SST
  • Supersonic
  • Surrogate modelling

ASJC Scopus subject areas

  • Computational Mathematics

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