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Advantages of surrogate models for architectural design optimization

Published online by Cambridge University Press:  07 October 2015

Thomas Wortmann ,
Alberto Costa ,
Giacomo Nannicini  and
Thomas Schroepfer
Thomas Wortmann*
Affiliation:
Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore
Alberto Costa
Affiliation:
Engineering Systems and Design, Singapore University of Technology and Design, Singapore
Giacomo Nannicini
Affiliation:
Engineering Systems and Design, Singapore University of Technology and Design, Singapore
Thomas Schroepfer
Affiliation:
Architecture and Sustainable Design, Singapore University of Technology and Design, Singapore
*
Reprint requests to: Thomas Wortmann, Architecture and Sustainable Design, Singapore University of Technology and Design, 8 Somapah Road, Singapore487372. E-mail: thomas_wortmann@mymail.sutd.edu.sg
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Abstract

Climate change, resource depletion, and worldwide urbanization feed the demand for more energy and resource-efficient buildings. Increasingly, architectural designers and consultants analyze building designs with easy-to-use simulation tools. To identify design alternatives with good performance, designers often turn to optimization methods. Randomized, metaheuristic methods such as genetic algorithms are popular in the architectural design field. However, are metaheuristics the best approach for architectural design problems that often are complex and ill defined? Metaheuristics may find solutions for well-defined problems, but they do not contribute to a better understanding of a complex design problem. This paper proposes surrogate-based optimization as a method that promotes understanding of the design problem. The surrogate method interpolates a mathematical model from data that relate design parameters to performance criteria. Designers can interact with this model to explore the approximate impact of changing design variables. We apply the radial basis function method, a specific type of surrogate model, to two architectural daylight optimization problems. These case studies, along with results from computational experiments, serve to discuss several advantages of surrogate models. First, surrogate models not only propose good solutions but also allow designers to address issues outside of the formulation of the optimization problem. Instead of accepting a solution presented by the optimization process, designers can improve their understanding of the design problem by interacting with the model. Second, a related advantage is that designers can quickly construct surrogate models from existing simulation results and other knowledge they might possess about the design problem. Designers can thus explore the impact of different evaluation criteria by constructing several models from the same set of data. They also can create models from approximate data and later refine them with more precise simulations. Third, surrogate-based methods typically find global optima orders of magnitude faster than genetic algorithms, especially when the evaluation of design variants requires time-intensive simulations.

Keywords

Computer-Aided Design ToolDesign OptimizationDesign Space ExplorationRadial Basis Function MethodSurrogate-Based Optimization
Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 471 - 481
Copyright
Copyright © Cambridge University Press 2015 

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