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A fast genetic algorithm for solving architectural design optimization problems

Published online by Cambridge University Press:  07 October 2015

Zhouzhou Su  and
Wei Yan
Zhouzhou Su
Affiliation:
Department of Architecture, Texas A&M University, Texas A&M University, College Station, Texas, USA
Wei Yan*
Affiliation:
Department of Architecture, Texas A&M University, Texas A&M University, College Station, Texas, USA
*
Reprint requests to: Wei Yan, Department of Architecture, Langford A 406, Texas A&M University, College Station, TX 77843, USA. E-mail: wyan@tamu.edu
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Abstract

Building performance simulation and genetic algorithms are powerful techniques for helping designers make better design decisions in architectural design optimization. However, they are very time consuming and require a significant amount of computing power. More time is needed when two techniques work together. This has become the primary impediment in applying design optimization to real-world projects. This study focuses on reducing the computing time in genetic algorithms when building simulation techniques are involved. In this study, we combine two techniques (offline simulation and divide and conquer) to effectively improve the run time in these architectural design optimization problems, utilizing architecture-specific domain knowledge. The improved methods are evaluated with a case study of a nursing unit design to minimize the nurses’ travel distance and maximize daylighting performance in patient rooms. Results show the computing time can be saved significantly during the simulation and optimization process.

Keywords

Architectural Design OptimizationBuilding Performance SimulationComputing TimeGenetic Algorithm

Information

Type
Special Issue Articles
Information
AI EDAM , Volume 29 , Special Issue 4: Generative and evolutionary design exploration , November 2015 , pp. 457 - 469
Copyright
Copyright © Cambridge University Press 2015 

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References

REFERENCES

Abboud, K., & Schoenauer, M. (2002). Surrogate deterministic mutation: preliminary results. In Artificial Evolution (Collet, P., Fonlupt, C., Hao, J.-K., Lutton, E., & Schoenauer, M., Eds.), pp. 104–116. Berlin: Springer.CrossRefGoogle Scholar
Ahn, K.-U., Young-Jin, K., Deu-Woo, K., Sung-Hwan, Y., & Cheol-Soo, P. (2013). Difficulties and issues in simulation of a high-rise office building. Proc. 13th Conf. Int. Building Performance Simulation Association, pp. 842–831, Chambéry, France, August 26–28.CrossRefGoogle Scholar
Anderson, K.S., & Hsu, Y. (1999). Genetic crossover strategy using an approximation concept. Proc. 1999 Congr. Evolutionary Computation, CEC ’99, Vol. 1. Washington, DC: IEEE.Google Scholar
Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., & Zaharia, M. (2010). A view of cloud computing. Communications of the ACM 53(4), 50–58.CrossRefGoogle Scholar
ASHRAE. (2007). ANSI/ASHRAE/IESNA Standard 90.1-2007, Energy Standard for Buildings Except Low-Rise Residential Buildings. Atlanta, GA: Author.Google Scholar
Attia, S., Gratia, E., De Herde, A., & Hensen, J.L. (2012). Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings 49, 2–15.CrossRefGoogle Scholar
Bellman, R. (1956). Dynamic programming and Lagrange multipliers. Proceedings of the National Academy of Sciences 42(10), 767.CrossRefGoogle ScholarPubMed
Besserud, K., Skidmore, O., & Merrill, L.L.P. (2008). Architectural genomics. Silicon + Skin: Biological Process and Computation: Proc. 28th Annual Conf. Association for Computer-Aided Design in Architecture (ACADIA), pp. 238–245, Minneapolis, MN, October 13–19.CrossRefGoogle Scholar
Buche, D., Schraudolph, N.N., & Koumoutsakos, P. (2005). Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35(2), 183–194.CrossRefGoogle Scholar
Chen, K.C., & Chen, C.Y.C. (2011). Stroke prevention by traditional Chinese medicine? A genetic algorithm, support vector machine and molecular dynamics approach. Soft Matte 7(8), 4001–4008.CrossRefGoogle Scholar
Choudhary, R. (2004). A hierarchical optimization framework for simulation-based architectural design. PhD Thesis. University of Michigan.Google Scholar
Choudhary, R., & Michalek, J. (2005). Design optimization in computer aided architectural design. Proc. CAADRIA, pp. 149–158. New Delhi, India: Association for Computer-Aided Architectural Design Research in Asia.Google Scholar
Choudhary, R., Malkawi, A., & Papalambros, P.Y. (2003). A hierarchical design optimization framework for building performance analysis. Proc. 8th IBPSA Conf. Eindhoven, The Netherlands: IBPSA.Google Scholar
Claussnitzer, S., Katz, N., Shaxted, M., Park, S.K., & Yori, R. (2014). Workshop—high-throughput computing (HTC) for parametric exploration by SOM. Proc. Annual Conf. Association for Computer Aided Design in Architecture (ACADIA), Los Angeles, October 23–25.Google Scholar
Coffey, B. (2012). Using building simulation and optimization to calculate lookup tables for control. PhD Thesis. University of California, Berkeley.Google Scholar
Coffey, B. (2013). Approximating model predictive control with existing building simulation tools and offline optimization. Journal of Building Performance Simulation 6(3), 220–235.CrossRefGoogle Scholar
Corbin, C.D., Henze, G.P., & May-Ostendorp, P. (2013). A model predictive control optimization environment for real-time commercial building application. Journal of Building Performance Simulation 6(3), 159–174.CrossRefGoogle Scholar
Deb, K. (2012). Optimization for Engineering Design: Algorithms and Examples. New Delhi: Prentice Hall.Google Scholar
Forrester, A.I., Bressloff, N.W., & Keane, A.J. (2006). Optimization using surrogate models and partially converged computational fluid dynamics simulations. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science 462(2071), 2177–2204.CrossRefGoogle Scholar
Gallas, M., & Halin, G. (2013). DaylightGen: from daylight intentions to architectural solutions. Proc. 31st eCAADe Conf., Vol. 2, pp. 107–116, Delft, The Netherlands, September 18–20.CrossRefGoogle Scholar
Gen, M., & Cheng, R. (2000). Genetic Algorithms and Engineering Optimization, Vol. 7. New York: Wiley.Google Scholar
Gerber, D.J., Lin, S.H.E., Pan, B.P., & Solmaz, A.S. (2012). Design optioneering: multi-disciplinary design optimization through parameterization, domain integration and automation of a genetic algorithm. Proc. 2012 Symp. Simulation for Architecture and Urban Design, p. 11. Orlando, FL, March 26–30.Google Scholar
Gero, J.S., & Louis, S.J. (1995). Improving Pareto optimal designs using genetic algorithms. Computer-Aided Civil and Infrastructure Engineering 10(4), 239–247.CrossRefGoogle Scholar
Gole, A.M. (2000). Simulation tools for system transients: an introduction. Proc. Power Engineering Society Summer Meeting, 2000, Vol. 2, pp. 761–762. Seattle, WA: IEEE.CrossRefGoogle Scholar
Hendrich, A., Chow, M., Skierczynski, B., & Lu, Z. (2008). A 36-hospital time and motion study: how do medical–surgical nurses spend their time? Permanente Journal 12(3), 25–34.CrossRefGoogle ScholarPubMed
Holland, J.H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. Ann Arbor, MI: University of Michigan Press.Google Scholar
Hong, T., Chou, S.K., & Bong, T.Y. (2000). Building simulation: an overview of developments and information sources. Building and Environment 35(4), 347–361.CrossRefGoogle Scholar
Hu, J., & Karava, P. (2014). Model predictive control strategies for buildings with mixed-mode cooling. Building and Environment 71, 233–244.CrossRefGoogle Scholar
Jin, Y. (2005). A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3–12.CrossRefGoogle Scholar
Jin, Y. (2011). Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm and Evolutionary Computation 1(2), 61–70.CrossRefGoogle Scholar
Jin, Y., Olhofer, M., & Sendhoff, B. (2000). On evolutionary optimization with approximate fitness functions. Proc. GECCO, pp. 786–793, Las Vegas, NV, July 8–12.Google Scholar
Jo, J.H., & Gero, J.S. (2006). Space layout planning using an evolutionary approach. Artificial Intelligence in Engineering 12(3), 149–162.CrossRefGoogle Scholar
Kim, H.M. (2001). Target cascading in optimal system design. PhD Thesis. University of Michigan.Google Scholar
Kim, H.M., Rideout, D.G., & Papalambros, P.Y. (2001). Analytical target cascading in automotive vehicle design. Proc. 2001 ASME Design Automation Conf., Pittsburgh, PA, September 9–12.CrossRefGoogle Scholar
Kociecki, M., & Adeli, H. (2013). Two-phase genetic algorithm for size optimization of free-form steel space-frame roof structures. Journal of Constructional Steel Research 90, 283–296.CrossRefGoogle Scholar
Lobos, D., & Donath, D. (2010). The problem of space layout in architecture: a survey and reflections. Arquitetura Revista 6(2), 136–161.CrossRefGoogle Scholar
Mackenzie, C.A., & Gero, J.S. (1987). Learning design rules from decisions and performances. Artificial Intelligence in Engineering 2(1), 2–10.CrossRefGoogle Scholar
Mahmoodabadi, M.J., Safaie, A.A., Bagheri, A., & Nariman-Zadeh, N. (2013). A novel combination of particle swarm optimization and genetic algorithm for Pareto optimal design of a five-degree of freedom vehicle vibration model. Applied Soft Computing 13(5), 2577–2591.CrossRefGoogle Scholar
Muhlenbein, H. (1991). Evolution in time and space—the parallel genetic algorithm. In Foundations of Genetic Algorithms (Rawlins, G.J.E., Ed.), San Francisco, CA: Morgan Kaufmann.Google Scholar
Negendahl, K., Perkov, T., & Heller, A. (2014). Approaching sentient building performance simulation systems. Proc. eCAADe 2014, Newcastle, UK, September 10–12.CrossRefGoogle Scholar
Ong, Y.S., Nair, P.B., & Keane, A.J. (2003). Evolutionary optimization of computationally expensive problems via surrogate modeling. AIAA Journal 41(4), 687–696.CrossRefGoogle Scholar
Ong, Y.S., Nair, P.B., & Lum, K.Y. (2006). Max-min surrogate-assisted evolutionary algorithm for robust design. IEEE Transactions on Evolutionary Computation 10(4), 392–404.Google Scholar
Papalambros, P.Y. (2002). The optimization paradigm in engineering design: promises and challenges. Computer-Aided Design 34(12), 939–951.CrossRefGoogle Scholar
Portugal, V., & Guedes, M. (2012). Informed parameterization:optimization of building openings generation. Plea2012—28th Conf., Opportunities, Limits & Needs Towards an Environmentally Responsible Architecture, Lima, Perú. Accessed at http://www.plea2012.pe/pdfs/T07-20120130-0053.pdfGoogle Scholar
Potter, M.A., & De Jong, K.A. (1994). A cooperative coevolutionary approach to function optimization. In Parallel Problem Solving From Nature—PPSN III (Goos, G., Hartmanis, J., & van Leeuwen, J., Eds.), pp. 249–257. Berlin: Springer.CrossRefGoogle Scholar
Potter, M.A., & De Jong, K.A. (2000). Cooperative coevolution: an architecture for evolving coadapted subcomponents. Evolutionary Computation 8(1), 1–29.CrossRefGoogle ScholarPubMed
Radford, A.D., & Gero, J.S. (1987). Design by Optimization in Architecture, Building, and Construction. New York: Wiley.Google Scholar
Rahmani Asl, M., Zarrinmehr, S., & Yan, W. (2013). Towards BIM-based parametric building energy performance optimization. Proc. Association for Computer Aided Design in Architecture (ACADIA), pp. 101–108, Cambridge, ON, Canada, October 24–27.Google Scholar
Rawat, C.D., Shahani, A., Natu, N., Badami, A., & Hingorani, R. (2012). A genetic algorithm for VLSI floor planning. International Journal of Engineering Science & Advanced Technology 2(3), 412–415.Google Scholar
Renner, G., & Ekárt, A. (2003). Genetic algorithms in computer aided design. Computer-Aided Design 35(8), 709–726.CrossRefGoogle Scholar
Rutten, D. (2011). Define “fitness”. . . . Accessed at http://ieatbugsforbreakfast.wordpress.com/2011/03/07/define-fitness/Google Scholar
Shi, X. (2011). Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy 36(3), 1659–1667.CrossRefGoogle Scholar
Simpson, T.W., Mauery, T.M., Korte, J.J., & Mistree, F. (2001). Kriging models for global approximation in simulation-based multidisciplinary design optimization. AIAA Journal 39(12), 2233–2241.CrossRefGoogle Scholar
Su, Z., & Yan, W. (2014). Improving genetic algorithm for design optimization using architectural domain knowledge. Proc. Annual Conf. Association for Computer Aided Design in Architecture (ACADIA). Los Angeles, October 23–25.CrossRefGoogle Scholar
USGBC. (2009). Green Building Design and Construction. Washington, DC: Author.Google Scholar
Valenzuela, C.L., & Jones, A.J. (1993). Evolutionary divide and conquer (I): a novel genetic approach to the TSP. Evolutionary Computation 1(4), 313–333.CrossRefGoogle Scholar
Vidal, T., Crainic, T.G., Gendreau, M., Lahrichi, N., & Rei, W. (2012). A hybrid genetic algorithm for multidepot and periodic vehicle routing problems. Operations Research 60(3), 611–624.CrossRefGoogle Scholar
von Buelow, P., Falk, A., & Turrin, M. (2010). Optimization of structural form using a genetic algorithm to search associative parametric geometry. Proc. Structure and Architecture, Gulmarães, Portugal.Google Scholar
Wagner, T. (1993). A general decomposition methodology for optimal system design. PhD Thesis. University of Michigan.Google Scholar
Walch, J.M., Rabin, B.S., Day, R., Williams, J.N., Choi, K., & Kang, J.D. (2005). The effect of sunlight on postoperative analgesic medication use: a prospective study of patients undergoing spinal surgery. Psychosomatic Medicine 67(1), 156–163.CrossRefGoogle ScholarPubMed
Wang, W., Zmeureanu, R., & Rivard, H. (2005). Applying multiple-objective genetic algorithms in green building design optimization. Building and Environment 40(11), 1512–1525.CrossRefGoogle Scholar
Watson, R.A. (2002). Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis. Proc. 8th Int. Conf. Parallel Problem Solving from Nature (PPSN-VIII),) pp. 161–171. Berlin: Springer.Google Scholar
Welle, B., Rogers, Z., & Fischer, M. (2012). BIM-Centric Daylight Profiler for Simulation (BDP4SIM): a methodology for automated product model decomposition and recomposition for climate-based daylighting simulation. Building and Environment 58, 114–134.CrossRefGoogle Scholar
Yu, Z., & Dexter, A. (2009). Simulation based predictive control of low-energy building systems using two-stage optimization. Proc. IBPSA'09, pp. 1562–1568, Glasgow, Scotland, July 27–30, 2009.Google Scholar
Zhou, Z., Ong, Y.S., Nair, P.B., Keane, A.J., & Lum, K.Y. (2007). Combining global and local surrogate models to accelerate evolutionary optimization. IEEE Transaction on Systems, Man, and Cybernetics, Part C: Applications and Reviews 37(1), 66–76.CrossRefGoogle Scholar
Zimring, C., Joseph, A., & Choudhary, R. (2004). The Role of the Physical Environment in the Hospital of the 21st Century: A Once-in-a-Lifetime Opportunity. Concord, CA: Center for Health Design.Google Scholar