Evidence-based robust optimisation of space systems with evidence network models

Gianluca Filippi, Massimiliano Vasile, Mariapia Marchi, Paolo Vercesi

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

4 Citations (Scopus)
115 Downloads (Pure)

Abstract

The paper presents an approach to optimise complex systems in space systems engineering, accounting for epistemic uncertainty. Uncertainty is modelled with Dempster-Shafer theory of Evidence and the space system as a network of connected components. A constrained min-max problem is then solved, with a memetic algorithm, to deliver a robust design point. Starting from this robust design point a sequence of evolutionary optimisation steps are used to reconstruct an approximation of the Belief and Plausibility curves associated to a particular design solution. The constrained min-max approach and the evolutionary reconstruction of the Belief and Plausibility curves are tested on one realistic case study of space systems engineering.
Original languageEnglish
Title of host publication2018 IEEE Congress on Evolutionary Computation
Place of PublicationPiscataway, N.J.
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Print)9781509060177
DOIs
Publication statusPublished - 4 Oct 2018
Event2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 IEEE Congress on Evolutionary Computation, CEC 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

Keywords

  • space system engineering
  • evidence network models
  • Belief functions

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  • Cite this

    Filippi, G., Vasile, M., Marchi, M., & Vercesi, P. (2018). Evidence-based robust optimisation of space systems with evidence network models. In 2018 IEEE Congress on Evolutionary Computation (pp. 1-8). Piscataway, N.J.: IEEE. https://doi.org/10.1109/CEC.2018.8477917