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Download fileConstrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation
journal contribution
posted on 2015-08-19, 13:31 authored by Alexander E.I. Brownlee, Jonathan WrightJonathan WrightReducing 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 ComputingVolume
33Pages
114 - 126Citation
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
2015-04-02Publication date
2015-04-17Notes
This is an open access article published by Elsevier under the CC BY license (http://creativecommons.org/licenses/by/4.0/).ISSN
1568-4946Publisher version
Language
- en