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Multi-disciplinary optimisation of building spatial designs is characterised by large solution spaces. Here two approaches are introduced, one being super-structured and the other super-structure free. Both are different in nature and perform differently for large solution spaces and each requires its own representation of a building spatial design, which are also presented here. A method to combine the two approaches is proposed, because the two are prospected to supplement each other. Accordingly a toolbox is presented, which can evaluate the structural and thermal performances of a building spatial design to provide a user with the means to define optimisation procedures. A demonstration of the toolbox is given where the toolbox has been used for an elementary implementation of a simulation of co-evolutionary design processes. The optimisation approaches and the toolbox that are presented in this paper will be used in future efforts for research into- and development of optimisation methods for multi-disciplinary building spatial design optimisation.

Building design can be supported effectively by computer-aided design exploration. This paper investigates optimisation based on a mixed-integer super-structure representation of the search space of building spatial designs. It can take into account parametric as well as topological variations. In the suggested super-structure – the so-called supercube representation – discrete and continuous variables determine the existence, respectively, dimensioning of spaces of the building spatial design. Constraints are formulated as closed form equations and can be used to numerically assess the feasibility of designs. A population-based constraint-handling evolutionary strategy is developed. In the constraint handling repair and penalty methods are combined in a domain specific way. The method is tested on different search space sizes and first promising results are reported.