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|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation|
|Author(s):||Brownlee, Alexander E I|
Wright, Jonathan A
|Citation:||Brownlee AEI & Wright JA (2015) Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation. Applied Soft Computing, 33, pp. 114-126. https://doi.org/10.1016/j.asoc.2015.04.010|
|Abstract:||Reducing building energy demand is a crucial part of the 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 Non-Dominated 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.|
|Rights:||This article is open-access. Open access publishing allows free access to and distribution of published articles where the author retains copyright of their work by employing a Creative Commons attribution licence. Proper attribution of authorship and correct citation details should be given.|
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