Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary\nalgorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool\nfor this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive:\noptimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate\nfitness models are a possible solution to this problem, but few approaches have been demonstrated\nfor multi-objective, constrained or discrete problems, typical of the optimisation problems in building\ndesign. This paper presents a modified version of a surrogate based on radial basis function networks,\ncombined with a deterministic scheme to deal with approximation error in the constraints by allowing\nsome infeasible solutions in the population. Different combinations of these are integrated with Non-\nDominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building\noptimisation problem. The comparisons show that the surrogate and constraint handling combined offer\nimproved run-time and final solution quality. The paper concludes with detailed investigations of the\nconstraint handling and fitness landscape to explain differences in performance.
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