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Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

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journal contribution
posted on 19.08.2015, 13:31 by Alexander E.I. Brownlee, Jonathan WrightJonathan Wright
Reducing building energy demand is a crucial part ofthe global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with NonDominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.

Funding

EPSRC [grant number: TS/H002782/1]

History

School

  • Architecture, Building and Civil Engineering

Published in

Applied Soft Computing

Volume

33

Pages

114 - 126

Citation

BROWNLEE, A.E.I. and WRIGHT, J.A., 2015. Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation. Applied Soft Computing, 33, pp. 114 - 126.

Publisher

© The Authors. Published by Elsevier B.V.

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

02/04/2015

Publication date

2015-04-17

Notes

This is an open access article published by Elsevier under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

ISSN

1568-4946

Language

en