High-lift devices topology optimisation using structured-chromosome genetic algorithm

Lorenzo Gentile, Elisa Morales, Edmondo Minisci, Domenico Quagliarella, Thomas Bartz-Beielstein, Renato Tognaccini

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This paper addresses the problem of including the choice of the High-Lift Devices (HLDs) configuration as a decision variable of an automatic optimisation tool. This task requires the coupling of an estimation routine and an optimisation algorithm. For the former, SU2 flow solver has been used. The Structured-Chromosome Genetic Algorithm (SCGA) optimiser has been employed to search for the optimal HLD. SCGA can overcome the limitations dictated by standard fixed-size continuous optimisation algorithms. Indeed, using hierarchical formulations, it can manage configurational decisions that are conventionally the responsibility of expert designers. The search algorithm bases its strategy on revised genetic operators conceived for handling hierarchical search spaces. The presented research not only shows the practicability of delegating to a specialised optimisation algorithm the complete HLD design but is intended to be a proof of concept for the whole field of multidisciplinary design optimisation. Indeed, the aerospace sector as a whole would benefit by reducing human intervention from the decision process.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway, NJ.
Number of pages9
ISBN (Print)9781728169309
Publication statusPublished - 3 Sep 2020
EventCEC 2020: IEEE Conference on Evolutionary computation - Glasgow, United Kingdom
Duration: 19 Jun 202024 Jul 2020


ConferenceCEC 2020: IEEE Conference on Evolutionary computation
Abbreviated titleCEC 2020
CountryUnited Kingdom


  • aerospace engineering
  • high-lift devices
  • optimisation
  • genetic algorithms
  • mixed-variable
  • variable-size

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