Ideation and Abstraction in Design Optimization

29Apr15

DESIGN OPTIMIZATION

Design optimization tools are creation tools that use parametric modeling, performance simulation and mathematical optimization to systematically generate and evaluate design alternatives (Holtzer et al. 2007). Design optimization, also known as design optioneering (Gerber et al. 2012) and computational design (Arieff 2013), is a departure from traditional architecture and engineering practice. Typically, architects generate a relatively small set of design alternatives that represent specific points in a multi-dimensional design space (Flager and Haymaker 2007). In architecture, this small set of design alternatives may be communicated in the form of two or three laser physical cut scale models or a few dozen digital photo- realistic visualizations. Even with the support of state-of- the-art computer-aided design tools (CAD), individual

designs are iterated relatively slowly and with considerable design effort. (Ibid.)

Conversely, architects and engineers using design optimization practices generate orders of magnitude more design alternatives by specifying design objectives in the form of design parameters and parameter ranges (Tsigkari et al. 2013). They use stochastic search methods, such as genetic algorithms, to automatically and iteratively compute large sets of design alternatives (Holzer et al. 2007). The designs that best fit the architects or engineer’s predefined acceptance criteria survive multiple generations to spawn successive generations of unique, new designs.

Contrasting with traditional design practices, optimized designs are computed parametrically and bred algorithmically. The numerous design alternatives that are produced are often represented by a multi-dimensional plot of solutions and might be coupled with a matrix of thumbnails of rendered designs. Researchers investigating the approach argue that design optimization enables designers to “more efficiently, and with more certainty, explore complex and tightly coupled design solution spaces” (Ibid.) than traditional design practices.

Ideation and Abstraction in Design Optimization

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