This is an open access article published by Elsevier under the CC BY license (http://creativecommons.org/licenses/by/4.0/).Reducing building energy demand is a crucial part ofthe global response to climate change, and evolutionary\ud algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool\ud for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive:\ud optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate\ud fitness models are a possible solution to this problem, but few approaches have been demonstrated\ud for multi-objective, constrained or discrete problems, typical of the optimisation problems in building\ud design. This paper presents a modified version of a surrogate based on radial basis function networks,\ud combined with a deterministic scheme to deal with approximation error in the constraints by allowing\ud some infeasible solutions in the population. Different combinations of these are integrated with NonDominated\ud Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building\ud optimisation problem. The comparisons show that the surrogate and constraint handling combined offer\ud improved run-time and final solution quality. The paper concludes with detailed investigations of the\ud constraint handling and fitness landscape to explain differences in performance

This paper describes the multi-objective optimized design of fenestration that is based on the façade of the building being divided into a number of small regularly spaced cells. The minimization of energy use and capital cost by a multi-objective genetic algorithm was investigated for: two alternative problem encodings (bit-string and integer); the application of constraint functions to control the aspect ratio of the windows; and the seeding of the search with feasible design solutions. It is concluded that the optimization approach is able to find near locally Pareto optimal solutions that have innovative architectural forms. Confidence in the optimality of the solutions was gained through repeated trail optimizations and a local search and sensitivity analysis. It was also concluded that seeding the optimization with feasible solutions was important in obtaining the optimum solutions when the window aspect ratio was constrained.