Abstract
Increasing applications of parametric design and performance simulations by architectural designers present opportunities to design more resource and energy efficient buildings via optimization. But Architectural Design Optimization (ADO) is less widespread than one might expect, due to, among other challenges, (1) lacking knowledge on simulation-based optimization, (2) a bias towards inefficient optimization methods—such as genetic algorithms (GAs)—in the building optimization literature, (3) lacking state-of-the-art, easy-to-use optimization tools, and, perhaps most importantly, (4) the problematic integration of optimization with architectural design. This problematic integration stems from a contrast between “wicked” or “co-evolving” architectural design problems, which exhibit vague and changing problem definitions, and optimization problems, which require problem definitions to be explicit and unchanging. This thesis presents an interdisciplinary study of ADO that draws on design theory, building optimization, mathematical optimization, and multivariate visualization. To address the first three challenges, the thesis (1) surveys existing optimization methods and benchmark results from the mathematical and building optimization literatures, (2) benchmarks a representative set of optimization methods on seven problems that involve structural, energy, and daylighting simulations, and (3) provides Opossum, a state-of-the-art, easy-to-use optimization tool. Opossum employs RBFOpt, a model-based optimization method that simultaneously “machine-learns” the shapes of fitness landscapes while searching for well-performing design candidates. RBFOpt emerges as the most efficient optimization method from the benchmark, and the GA as the least efficient. To mitigate the contrast between architectural and optimization problems, the thesis (4) proposes performance-informed design space exploration (DSE), a novel concept that emphasizes selection, refinement, and understanding over finding highest-performing design candidates, (5) presents Performance Maps, a novel visualization method for fitness landscapes, (6) implements Performance Maps in the Performance Explorer, an interactive, visual tool for performance-informed DSE, and (7) evaluates the Performance Explorer through a user test with thirty participants. The Performance Explorer emerges as more supportive and enjoyable to use than manual search and/or optimization from this test. In short, the thesis offers tools for ADO and performance-informed DSE that are more efficient and that better acknowledge the “wickedness” of architectural design problems