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

Gianluca Filippi; Mariapia Marchi; Massimiliano Vasile; Paolo Vercesi

doi:10.1109/CEC.2018.8477917

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

Gianluca Filippi, Mariapia Marchi, Massimiliano Vasile, Paolo Vercesi

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

6 Citations (Scopus)

120 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 language	English
Title of host publication	2018 IEEE Congress on Evolutionary Computation
Place of Publication	Piscataway, N.J.
Publisher	IEEE
Number of pages	8
ISBN (Print)	9781509060177
DOIs	https://doi.org/10.1109/CEC.2018.8477917
Publication status	Published - 4 Oct 2018
Event	2018 IEEE Congress on Evolutionary Computation, CEC 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018

Conference

Conference	2018 IEEE Congress on Evolutionary Computation, CEC 2018
Country/Territory	Brazil
City	Rio de Janeiro
Period	8/07/18 → 13/07/18

Keywords

space system engineering
evidence network models
Belief functions

Access to Document

10.1109/CEC.2018.8477917

Filippi-etal-IEEE-2018-Evidence-based-robust-optimisation-of-space-systems-with-evidenceAccepted author manuscript, 458 KB

1 Finished

Uncertainty Treatment and OPtimisation in Aerospace Engineering (UTOPIAE) (H2020 MCSA ETN)
Vasile, M., Akartunali, K., Maddock, C., Minisci, E. & Revie, M.
European Commission - Horizon 2020
1/01/17 → 31/12/20
Project: Research

Cite this

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title = "Evidence-based robust optimisation of space systems with evidence network models",

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.",

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author = "Gianluca Filippi and Mariapia Marchi and Massimiliano Vasile and Paolo Vercesi",

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AU - Marchi, Mariapia

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AU - Vercesi, Paolo

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N2 - 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.

AB - 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.

KW - space system engineering

KW - evidence network models

KW - Belief functions

U2 - 10.1109/CEC.2018.8477917

DO - 10.1109/CEC.2018.8477917

M3 - Conference contribution book

AN - SCOPUS:85056274179

SN - 9781509060177

BT - 2018 IEEE Congress on Evolutionary Computation

PB - IEEE

CY - Piscataway, N.J.

T2 - 2018 IEEE Congress on Evolutionary Computation, CEC 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

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

Abstract

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Keywords

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Projects

Uncertainty Treatment and OPtimisation in Aerospace Engineering (UTOPIAE) (H2020 MCSA ETN)

Cite this