Preliminary spacecraft design by means of structured-chromosome genetic algorithms

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Abstract

This paper presents a new methodology for complex system design by means of optimisation techniques. Within the Model-based Engineering approach, optimisation algorithms are used to explore optimal solutions of highly coupled and nonlinear systems. In such scenario, the optimal technology has to be identified and its settings have to be optimised. Relying on optimisation strategies for both the challenges brings to complex mixed-variable problem formulations involving continuous, integer and categorical parameters. Furthermore, part of the parameters are required only if certain technologies are adopted, bringing to variable-size formulations that standard optimisers cannot manage. Therefore, the proposed methodology relies on the use of variable-size mixed-variable global optimiser Structured-Chromosome Genetic Algorithm (SCGA). The advantages of this new method are shown by applying it for solving a space system preliminary design. In particular, two variants have been implemented distinguished by two different levels of complexity. To better appreciate the proposed approach, the same problems have been reformulated to be treated by a well known and appreciated optimiser in the field of spacecraft design, Multi– Population Adaptive Inflationary Differential Evolution Algorithm (MP–AIDEA). The final results of the two approaches are compared and commented.
Original languageEnglish
Title of host publication2020 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages9
ISBN (Print)9781728169309
DOIs
Publication statusPublished - 3 Sep 2020
EventCEC 2020: IEEE Conference on Evolutionary computation - Glasgow, United Kingdom
Duration: 19 Jun 202024 Jul 2020

Conference

ConferenceCEC 2020: IEEE Conference on Evolutionary computation
Abbreviated titleCEC 2020
CountryUnited Kingdom
CityGlasgow
Period19/06/2024/07/20

Keywords

  • space systems
  • optimisation
  • mixed-variable
  • variable-size

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