Distributed aero-engine control systems architecture selection using multi-objective optimisation

Thompson, Haydn A.; Chipperfield, A.J.; Fleming, PJ; Legge, C.

doi:10.1016/s0967-0661(99)00011-8

Cited by 15 publications

(1 citation statement)

References 2 publications

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“…Star and ring are depicted in Figure 6 and Figure 7 below. In a study of different connection topologies, Thompson, et al 5 , concluded that the ring topology minimizes harness weight with maximum reliability.…”

Section: A the Impact On Weight Reductionmentioning

confidence: 99%

Concepts for Distributed Engine Control

Culley

Thomas²,

Saus³

2007

43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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Section: A the Impact On Weight Reductionmentioning

confidence: 99%

Concepts for Distributed Engine Control

Culley

Thomas²,

Saus³

2007

43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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Real World System Architecture Design Using Multi-criteria Optimization: A Case Study

Kudikala

Mills

Fleming

et al. 2013

EVOLVE - A Bridge Between Probability, Set Oriented Numerics, and Evolutionary Computation IV

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Generalized decomposition and cross entropy methods for many-objective optimization

Giagkiozis

Purshouse

Fleming

2014

Information Sciences

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105

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Monograph:Giagkiozis, I., Purshouse, R.C. and Fleming, P.J. (2012) ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract-Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms, the problem of selecting a way to distribute or guide these solutions in a high-dimensional space has not been explored. In this work, we introduce a novel concept which we call generalized decomposition. Generalized decomposition provides a framework with which the decision maker (DM) can guide the underlying evolutionary algorithm toward specific regions of interest or the entire Pareto front with the desired distribution of Pareto optimal solutions. Additionally, it is shown that generalized decomposition simplifies many-objective problems by unifying the three performance objectives of multi-objective evolutionary algorithms -convergence to the PF, evenly distributed Pareto optimal solutions and coverage of the entire front -to only one, that of convergence. A framework, established on generalized decomposition, and an estimation of distribution algorithm (EDA) based on low-order statistics, namely the cross-entropy method (CE), is created to illustrate the benefits of the proposed concept for many objective problems. This choice of EDA also enables the test of the hypothesis that low-order statistics based EDAs can have comparable performance to more elaborate EDAs.

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Distributed aero-engine control systems architecture selection using multi-objective optimisation

Cited by 15 publications

References 2 publications

Concepts for Distributed Engine Control

Concepts for Distributed Engine Control

Real World System Architecture Design Using Multi-criteria Optimization: A Case Study

Generalized decomposition and cross entropy methods for many-objective optimization

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