Convergence toward optimal building designs via multiple iterative local optimizaitons applied to attribute clusters of design variants

Abstract

The present paper explores the potential of a novel approach toward iterative global optimization of locally optimized attribute clusters of building design solutions. Thereby, clusters of design space attributes (i.e., sets of ontologically cognate aspects of designs) are made subject to multiple passes of local simulation-assisted optimization runs. Hence, instead of allocating an individual dimension to each and every variable of a complex design within the context of a single-pass global optimization campaign, multiple iterative optimization steps target coherent clusters of such attributes and pursue those until the overall design meets the expected performance (or until further performance improvement is not forthcoming).