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Design mining interacting wind turbines

Preen, Richard J.; Preen, Richard; Bull, Larry

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Authors

Richard J. Preen

Dr Richard Preen Richard2.Preen@uwe.ac.uk
Senior Research Fellow in Machine Learning

Lawrence Bull Larry.Bull@uwe.ac.uk
School Director (Research & Enterprise) and Professor



Abstract

© 2016 by the Massachusetts Institute of Technology. An initial study has recently been presented of surrogate-assisted evolutionary algorithms used to design vertical-axis wind turbines wherein candidate prototypes are evaluated under fan-generated wind conditions after being physically instantiated by a 3D printer. Unlike other approaches, such as computational fluid dynamics simulations, no mathematical formulations were used and no model assumptions weremade. This paper extends that work by exploring alternative surrogate modelling and evolutionary techniques. The accuracy of various modelling algorithms used to estimate the fitness of evaluated individuals from the initial experiments is compared. The effect of temporally windowing surrogate model training samples is explored. A surrogateassisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.

Journal Article Type Article
Online Publication Date Mar 10, 2016
Publication Date Mar 1, 2016
Deposit Date Jun 22, 2015
Publicly Available Date Jun 10, 2016
Journal Evolutionary Computation
Print ISSN 1063-6560
Electronic ISSN 1530-9304
Publisher Massachusetts Institute of Technology Press (MIT Press)
Peer Reviewed Peer Reviewed
Volume 24
Issue 1
Pages 89-111
DOI https://doi.org/10.1162/EVCO_a_00144
Keywords 3-D printing, coevolution, fitness approximation, neural network, partnering
Public URL https://uwe-repository.worktribe.com/output/914728
Publisher URL http://dx.doi.org/10.1162/EVCO_a_00144
Contract Date Mar 11, 2016

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