title: Design Mining Interacting Wind Turbines.
abstract: 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 were made. 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 surrogate-assisted approach based on an enhanced local search is introduced; and alternative coevolution collaboration schemes are examined.
authors: Department of Computer Science and Creative Technologies, University of the West of England, Bristol, BS16 1QY, UK richard2.preen@uwe.ac.uk.
authors: Richard J
authors: RJ
authors: Preen
authors: Department of Computer Science and Creative Technologies, University of the West of England, Bristol, BS16 1QY, UK larry.bull@uwe.ac.uk.
authors: Larry
authors: L
authors: Bull
mesh_terms: D000465:Algorithms; D011211:Electric Power Supplies; D004867:Equipment Design; D008962:Models, Theoretical; D011210:Power Plants; D066330:Printing, Three-Dimensional; D059205:Renewable Energy; D014919:Wind
keywords: 3D printing; coevolution; fitness approximation; neural network; partnering.