Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation
journal contributionposted on 2015-08-19, 13:31 authored 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.
EPSRC [grant number: TS/H002782/1]
- Architecture, Building and Civil Engineering
Published inApplied Soft Computing
Pages114 - 126
CitationBROWNLEE, 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.
- VoR (Version of Record)
Publisher statementThis 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/
NotesThis is an open access article published by Elsevier under the CC BY license (http://creativecommons.org/licenses/by/4.0/).