Black-Box Optimization of Lighting Simulation in Architectural Design

This paper deals with an application of optimization in architectural design. Formally, we consider the problem of optimizing a function that can only be evaluated through an expensive oracle. We assume that the analytical expression of the function is unknown and first-order information is not available. This situation frequently occurs when each function evaluation relies on the output of a complex and time-consuming simulation. In the literature, this is called a black-box optimization problem with costly evaluation. This paper presents a black-box problem from architectural design: we aim to find the values of the design variables that yield optimal lighting conditions inside a building. The building facade is described as a parametric model whose parameters are the design variables.We tackle this problem by adapting the Radial Basis Function (RBF) method originally proposed by Gutmann (2001). Experiments indicate that our open-source implementation is competitive with commercial software for black-box optimization, and that it can be a valuable decision-support tool for complex problems requiring time-consuming simulations. The usefulness of this approach goes beyond the specific application in architectural design.

[1]  H. Zimmermann.  Towards global optimization 2: L.C.W. DIXON and G.P. SZEGÖ (eds.) North-Holland, Amsterdam, 1978, viii + 364 pages, US $ 44.50, Dfl. 100,-. , 1979 .

[2]  Allan Clark,et al.  Semantic-Based Development of Service-Oriented Systems , 2006, FORTE.

[3]  John Mardaljevic,et al.  Daylighting, Artificial Lighting and Non-Visual Effects Study for a Residential Building , 2012 .

[4]  Christoph F. Reinhart,et al.  DIVA 2.0: INTEGRATING DAYLIGHT AND THERMAL SIMULATIONS USING RHINOCEROS 3D, DAYSIM AND ENERGYPLUS , 2011 .

[5]  Robert F. Woodbury,et al.  Whither design space? , 2006, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[6]  Mattias Björkman,et al.  Global Optimization of Costly Nonconvex Functions Using Radial Basis Functions , 2000 .

[7]  David Jason Gerber,et al.  Designing-in performance: A framework for evolutionary energy performance feedback in early stage design , 2014 .

[8]  A. Kaveh,et al.  A new multi-swarm multi-objective optimization method for structural design , 2013, Adv. Eng. Softw..

[9]  Nils-Hassan Quttineh,et al.  An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization , 2008 .

[10]  Tomasz Arciszewski,et al.  Evolutionary computation and structural design: A survey of the state-of-the-art , 2005 .

[11]  H. Rittel,et al.  Dilemmas in a general theory of planning , 1973 .

[12]  John Christopher Miles,et al.  Genetic algorithms for design , 2010 .

[13]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..

[14]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[15]  Ali Rahim.  Contemporary techniques in architecture , 2002 .

[16]  Christine A. Shoemaker,et al.  Improved Strategies for Radial basis Function Methods for Global Optimization , 2007, J. Glob. Optim..

[17]  Rivka Oxman,et al.  Performance-Based Design: Current Practices and Research Issues , 2008 .

[18]  Kenneth Holmström,et al.  An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization , 2008, J. Glob. Optim..

[19]  Thomas Hemker,et al.  Derivative Free Surrogate Optimization for Mixed-Integer Nonlinear Black Box Problems in Engineering , 2010 .

[20]  Daniel Krob,et al.  Modelling of Complex Software Systems: A Reasoned Overview , 2006, FORTE.

[21]  Zenon Waszczyszyn.  Advances of Soft Computing in Engineering , 2010 .

[22]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[23]  Hans-Martin Gutmann,et al.  A Radial Basis Function Method for Global Optimization , 2001, J. Glob. Optim..