Skip to main content
SpringerLink
Log in
Book cover

City Networks pp 259–278Cite as

Introduction to Architectural Design Optimization

  • Chapter
  • First Online:

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 128))

Abstract

This chapter presents black-box (or derivative-free) optimization from the perspective of architectural design optimization. We introduce and compare single- and multi-objective optimization, discuss applications from architectural design and related fields, and survey the three main classes of black-box optimization algorithms: metaheuristics, direct search, and model-based methods. We also give an overview over optimization tools available to architectural designers and discuss criteria for choosing between different optimization algorithms. Finally, we survey recent benchmark results from both mathematical test problems and simulation-based problems from structural, building energy, and daylighting design. Based on these empirical results, we recommend the use of global direct search and model-based methods over metaheuristics such as genetic algorithms, especially when the budget of function evaluations is limited, for example, in the case of time-intensive simulations. When it is more important to understand the trade-off between performance criteria than to find good solutions and the budget of function evaluations is sufficient to approximate the Pareto front accurately, we recommend multi-objective, Pareto-based optimization algorithms.

This is a preview of subscription content, log in via an institution.

References

  1. Asl, M.R., Stoupine, A., Zarrinmehr, S., Yan, W.: Optimo: a BIM-based multi-objective optimization tool. In: Martens, B., Wurzer, G., Grasl, T., et al. (eds) Proceedings of the 33rd eCAADe Conference. eCAADe and TU Wien, Vienna, AUT, pp. 673–682 (2015)

    Google Scholar 

  2. Attia, S., Hamdy, M., O’Brien, W., Carlucci, S.: Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energ. Buildings. 60, 110–124 (2013)

    Article  Google Scholar 

  3. Audet, C., Dennis, J.E.: Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17, 188–217 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  4. Audet, C., Savard, G., Zghal, W.: A mesh adaptive direct search algorithm for multiobjective optimization. Eur. J. Oper. Res. 204, 545–556 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  5. Bader, J., Zitzler, E.: HypE: an Algorithm for Fast Hypervolume-Based Many-Objective Optimization. Computer Engineering and Networks Laboratory (TIK). Department of Electrical Engineering Swiss Federal Institute of Technology (ETH), Zurich (2008)

    Google Scholar 

  6. Besserud, K., Katz, N., Beghini, A.: Structural emergence: architectural and structural design collaboration at SOM. Archit. Des. 83, 48–55 (2013)

    Google Scholar 

  7. Beyer, H.-G., Schwefel, H.-P.: Evolution strategies – a comprehensive introduction. Nat. Comput. 1, 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  8. Binkley, C., Jeffries, P., Vola, M.: Design computation at Arup. In: Andia, A., Spiegelhalter, T. (eds.) Post-Parametric Automation in Design and Construction, pp. 113–119. Artech House, Boston/London (2014)

    Google Scholar 

  9. Bradner, E., Iorio, F., Davis, M.: Parameters tell the design story: ideation and abstraction in design optimization. In: Proceedings of SimAUD. SCS, p. 26 (2014)

    Google Scholar 

  10. Brownlee, A.E.I., Wright, J.A.: Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation. Appl. Soft Comput. 33, 114–126 (2015)

    Article  Google Scholar 

  11. Chiandussi, G., Codegone, M., Ferrero, S., Varesio, F.E.: Comparison of multi-objective optimization methodologies for engineering applications. Comput. Math. Appl. 63, 912–942 (2012)

    Google Scholar 

  12. Cichocka, J., Browne, W., Rodriguez, E.: Evolutionary optimization processes as design tools: implementation of a revolutionary swarm approach. In: Proceedings of 31th International PLEA Conference. Bologna, IT, (2015)

    Google Scholar 

  13. Coello, C.A.C.: A comprehensive survey of evolutionary-based multiobjective optimization techniques. Knowl. Inf. Syst. 1, 269–308 (2013)

    Article  Google Scholar 

  14. Conn, A., Scheinberg, K., Vicente, L.: Introduction to Derivative-Free Optimization. Society for Industrial and Applied Mathematics, Philadelphia (2009)

    Book  MATH  Google Scholar 

  15. Conn, A.R., Gould, N.I.M., Toint, P.L.: Trust Region Methods. Society for Industrial and Applied Mathematics, (2000)

    Google Scholar 

  16. Costa, A., Nannicini, G.: RBFOpt: an Open-Source Library for Black-box Optimization with Costly Function Evaluations. Singapore University of Technology and Design, Singapore (2014)

    Google Scholar 

  17. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6, 182–197 (2002)

    Article  Google Scholar 

  18. Dodangeh, M., Vicente, L.N.: Worst case complexity of direct search under convexity. Math. Program. 155, 307–332 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  19. Dynamo Studio: Autodesk. (2017)

    Google Scholar 

  20. Eisenhower, B., O’Neill, Z., Narayanan, S., et al.: A methodology for meta-model based optimization in building energy models. Energ. Buildings. 47, 292–301 (2012)

    Article  Google Scholar 

  21. Emmerich, M.T.M., Hopfe, C.J., Marijt, R., et al: Evaluating optimization methodologies for future integration in building performance tools. In: Proceedings of the 8th Int. Conf. on Adaptive Computing in Design and Manufacture (ACDM). p. 7 (2008)

    Google Scholar 

  22. Evins, R.: A review of computational optimisation methods applied to sustainable building design. Renew. Sust. Energ. Rev. 22, 230–245 (2013)

    Article  Google Scholar 

  23. Evins, R., Williams, C., Pointer, P., et al.: Multi-objective design optimisation: getting more for less. Proc. ICE – Civ. Eng. 165, 5–10 (2012)

    Google Scholar 

  24. Gerber, D.J., Lin, S.-H.E.: Designing in complexity: simulation, integration, and multidisciplinary design optimization for architecture. Simulation. 90, 936–959 (2014)

    Article  Google Scholar 

  25. Grasshopper®: Robert McNeel & Associates (2014)

    Google Scholar 

  26. Gutmann, H.-M.: A radial basis function method for global optimization. J. Glob. Optim. 19, 201–227 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  27. Hamdy, M., Nguyen, A.-T., Hensen, J.L.M.: A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems. Energ. Buildings. 121, 57–71 (2016)

    Article  Google Scholar 

  28. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evol. Comput. 9, 159–195 (2001)

    Article  Google Scholar 

  29. Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130, 449–467 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  30. Hare, W., Nutini, J., Tesfamariam, S.: A survey of non-gradient optimization methods in structural engineering. Adv. Eng. Softw. 59, 19–28 (2013)

    Article  Google Scholar 

  31. Hasançebi, O., Çarbaş, S., Doğan, E., et al.: Performance evaluation of metaheuristic search techniques in the optimum design of real size pin jointed structures. Comput. Struct. 87, 284–302 (2009)

    Article  Google Scholar 

  32. Heimrath, M.: ADO at Bollinger+Grohmann (2017)

    Google Scholar 

  33. Hendrix, E.M.T., Tóth, B.G.: Introduction to Nonlinear and Global Optimization. Springer, New York (2010)

    Book  MATH  Google Scholar 

  34. Hladik, P., Lewis, C.J.: Singapore national stadium roof. Int. J. Archit. Comput. 8, 257–277 (2010)

    Article  Google Scholar 

  35. Holland, J.H.: Genetic algorithms. Sci. Am. 267, 66–72 (1992)

    Article  Google Scholar 

  36. Holmström, K.: An adaptive radial basis algorithm (ARBF) for expensive black-box global optimization. J. Glob. Optim. 41, 447–464 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  37. Hooke, R., Jeeves, T.A.: “Direct search” solution of numerical and statistical problems. J. ACM. 8, 212–229 (1961)

    Article  MATH  Google Scholar 

  38. Huyer, W., Neumaier, A.: Global optimization by multilevel coordinate search. J. Glob. Optim. 14, 331–355 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  39. Huyer, W., Neumaier, A.: SNOBFIT – stable noisy optimization by branch and fit. ACM Trans. Math. Softw. 35, 9:1–9:25 (2008). https://doi.org/10.1145/1377612.1377613

  40. Imbert, F., Frost, K.S., Fisher, A., et al.: Concurrent geometric, structural and environmental design: louvre Abu Dhabi. In: Hesselgren, L., Sharma, S., Wallner, J., et al. (eds.) Advances in Architectural Geometry 2012, pp. 77–90. Springer, Vienna (2013)

    Chapter  Google Scholar 

  41. Johnson, S.G.: The NLopt nonlinear-optimization package. (2010)

    Google Scholar 

  42. Jones, D.R., Perttunen, C.D., Stuckman, B.E.: Lipschitzian optimization without the Lipschitz constant. J. Optim. Theory Appl. 79, 157–181 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  43. Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Glob. Optim. 13, 455–492 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  44. Kaelo, P., Ali, M.M.: Some variants of the controlled random search algorithm for global optimization. J. Optim. Theory Appl. 130, 253–264 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  45. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, 1995. Proceedings. pp. 1942–1948 vol. 4 (1995)

    Google Scholar 

  46. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science. 220, 671–680 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  47. Knowles, J.: ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems. IEEE Trans. Evol. Comput. 10, 50–66 (2006)

    Article  Google Scholar 

  48. Knowles, J., Nakayama, H.: Meta-modeling in multiobjective optimization. In: Branke, J., Deb, K., Miettinen, K., Słowiński, R. (eds.) Multiobjective Optimization, pp. 245–284. Springer, Berlin/Heidelberg (2008)

    Chapter  Google Scholar 

  49. Kolda, T., Lewis, R., Torczon, V.: Optimization by direct search: new perspectives on some classical and modern methods. SIAM Rev. 45, 385–482 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  50. Koziel, S., Ciaurri, D.E., Leifsson, L.: Surrogate-based methods. In: Koziel, S., Yang, X.-S. (eds.) Computational Optimization, Methods and Algorithms, pp. 33–59. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  51. Loshchilov, I., Glasmacher, T.: Black-Box Optimization Competition. bbcomp.ini.rub.de. (2017.) Accessed 14 Feb 2017

  52. Luebkeman, C., Shea, K.: CDO: computational design + optimization in building practice. Arup J. 3, 17–21 (2005)

    Google Scholar 

  53. Machairas, V., Tsangrassoulis, A., Axarli, K.: Algorithms for optimization of building design: a review. Renew. Sust. Energ. Rev. 31, 101–112 (2014)

    Article  Google Scholar 

  54. Malkawi, A.: Performance simulation. In: Kolarevic, B., Malkawi, A. (eds.) Performative Architecture beyond Instrumentality, pp. 85–95. Taylor and Francis, Hoboken (2005)

    Google Scholar 

  55. Marler, R.T., Arora, J.S.: Survey of multi-objective optimization methods for engineering. Struct. Multidiscip. Optim. 26, 369–395 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  56. Miles, J.: Genetic algorithms for design. In: Waszczyszyn, Z. (ed.) Advances of Soft Computing in Engineering, pp. 1–56. Springer, Vienna (2010)

    Google Scholar 

  57. Moré, J. J. and Wild, S. M.: 2009. Benchmarking derivative-free optimization algorithms. SIAM Journal on Optimization, 20 (1), 172–191

    Google Scholar 

  58. Müller, J., Shoemaker, C.A.: Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems. J. Glob. Optim. 60, 123–144 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  59. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  60. Nguyen, A.-T., Reiter, S., Rigo, P.: A review on simulation-based optimization methods applied to building performance analysis. Appl. Energy. 113, 1043–1058 (2014)

    Article  Google Scholar 

  61. Palonen, M., Hamdy, M., Hasan, A.: MOBO a new software for multi-objective building performance optimization. In: Proceedings of the 13th International Conference of the IBPSA. pp. 2567–2574 (2013)

    Google Scholar 

  62. Pasternak, A., Kwiecinski, K.: High-rise Building Optimization: A Design Studio Curriculum. In: Proceedings of the 33rd eCAADe Conference. eCAADe and Faculty of Architecture and Urban Planning, TU Wien, Vienna, AUT, pp. 305–314 (2015)

    Google Scholar 

  63. Powell, M.J.: A direct search optimization method that models the objective and constraint functions by linear interpolation. In: Gomez, S., Hennart, J.-P. (eds.) Advances in Optimization and Numerical Analysis, pp. 51–67. Springer, Dordrecht (1994)

    Chapter  Google Scholar 

  64. Powell, M.J.: The BOBYQA Algorithm for Bound Constrained Optimization without Derivatives. University of Cambridge, Cambridge, UK (2009)

    Google Scholar 

  65. Regis, R.G., Shoemaker, C.A.: A stochastic radial basis function method for the global optimization of expensive functions. Inf. J. Comput. 19, 497–509 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  66. Rios, L.M., Sahinidis, N.V.: Derivative-free optimization: a review of algorithms and comparison of software implementations. J. Glob. Optim. 56, 1247–1293 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  67. Rowan, T.H.: Functional stability analysis of numerical algorithms. Ph.D. Dissertation, The University of Texas (1990)

    Google Scholar 

  68. Rüdenauer, K., Dohmen, P.: Heuristic methods in architectural design optimization. In: 25th eCAADe Conference Proceedings, Frankfurt am Main, DE, pp. 507–614 (2007)

    Google Scholar 

  69. Rutten, D.: Evolutionary Principles Applied to Problem Solving. www.grasshopper3d.com/profiles/blogs/evolutionary-principles (2010) Accessed 14 Feb 2017

  70. Scheurer, F.: Getting complexity organised. Autom. Constr. 16, 78–85 (2007)

    Article  Google Scholar 

  71. Singh, S., Kensek, K.: Early design analysis using optimization techniques in design/practice. In: ASHRAE Conference Proceedings. ASHRAE, Atlanta, GA, (2014)

    Google Scholar 

  72. Steuer, R.E., Choo, E.-U.: An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Program. 26, 326–344 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  73. Talbi, E.-G.: Metaheuristics: from Design to Implementation. Wiley, Hoboken (2009)

    Book  MATH  Google Scholar 

  74. Turrin, M., von Buelow, P., Stouffs, R.: Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Adv. Eng. Inform. 25, 656–675 (2011)

    Article  Google Scholar 

  75. Vicente, L.N.: Worst case complexity of direct search. EURO. J. Comput. Optim. 1, 143–153 (2013)

    Article  MATH  Google Scholar 

  76. Vierlinger, R.: Multi objective design interface. Msc Thesis, Technische Universität Wien (2013)

    Google Scholar 

  77. Wetter, M.: GenOpt–a generic optimization program. In: Proceedings of the Seventh International IBPSA Conference. Rio de Janeiro, BR, pp. 601–608 (2001)

    Google Scholar 

  78. Wetter, M., Wright, J.: A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Build. Environ. 39, 989–999 (2004)

    Article  Google Scholar 

  79. Wild, S.M., Regis, R.G., Shoemaker, C.A.: ORBIT: optimization by radial basis function interpolation in trust-regions. SIAM J. Sci. Comput. 30, 3197–3219 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  80. Woodbury, R.F.: Elements of Parametric Design. Routledge, London/New York (2010)

    Google Scholar 

  81. Wortmann, T.: Opossum—a model-based optimization tool in grasshopper. In: Proceedings of the 22nd CAADRIA Conference, Hong Kong, CN, pp. 283–292 (2017)

    Google Scholar 

  82. Wortmann, T., Costa, A., Nannicini, G., Schroepfer, T.: Advantages of surrogate models for architectural design optimization. AIEDAM. 29, 471–481 (2015)

    Article  Google Scholar 

  83. Wortmann, T., Nannicini, G.: Black-box optimization for architectural design: An overview and quantitative comparison of metaheuristic, direct search, and model-based optimization methods. In: Chien, S.-F., Seungyeon, C., Schnabel, M.A., et al. (eds) Proceedings of the 21th CAADRIA Conference. CAADRIA, Hong Kong, pp. 177–186 (2016)

    Google Scholar 

  84. Wortmann, T., Waibel, C., Nannicini, G., et al: Are genetic algorithms really the best choice in building energy optimization? In: Proceedings of the Symposium on Simulation for Architecture & Urban Design. Toronto, CA (2017)

    Google Scholar 

  85. Yang, D., Sun, Y., Turrin, M., et al: Multi-objective and multidisciplinary design optimization of large sports building envelopes: a case study. In: Proceedings of the International Association for Shell and Spatial Structures (IASS) (2015)

    Google Scholar 

  86. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks Laboratory (TIK). Department of Electrical Engineering Swiss Federal Institute of Technology (ETH), Zurich (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Singapore University of Technology and Design, 138682, Singapore, Singapore

    Thomas Wortmann

  2. IBM T.J. Watson Research Center, 10598, Yorktown Heights, NY, USA

    Giacomo Nannicini

Authors
  1. Thomas Wortmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Giacomo Nannicini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Wortmann .

Editor information

Editors and Affiliations

  1. Department of Business Administration, Technological Educational Institute of Central Macedonia, Serres, Greece

    Athanasia Karakitsiou

  2. Industrial Logistics, Department of Business Administration, Technology and Social Sciences, Luleå University of Technology, Luleå, Sweden

    Athanasios Migdalas

  3. INSEAD, France

    Stamatina Th. Rassia

  4. Deparment of Industrial and Systems Engineering, University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Wortmann, T., Nannicini, G. (2017). Introduction to Architectural Design Optimization. In: Karakitsiou, A., Migdalas, A., Rassia, S., Pardalos, P. (eds) City Networks. Springer Optimization and Its Applications, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-65338-9_14

Download citation

Publish with us

Policies and ethics

Search

Navigation