Volume 10, Issue 3 (2020)
Naqshejahan 2020, 10(3): 205-217 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Pilechiha P. Optimization Methods and Algorithms in Architectural and Urban Design, Basic Mathematical Solutions. Naqshejahan 2020; 10 (3) :205-217
URL: http://bsnt.modares.ac.ir/article-2-42128-en.html
URL: http://bsnt.modares.ac.ir/article-2-42128-en.html
Architecture Department, Kowsar Higher Education, Qazvin, Iran , p.pilechiha@modares.ac.ir
Abstract: (5156 Views)
Building design is a quite complex activity where a team of designers working on diverse and contradictory parameters to make the balance between them. Because of this complexity, building performance simulation tools were developed and subsequently, the use of optimization methods, generally, as a decision-making tool is started.
The current study is a review of the optimization methods and algorithms which are used in the design of the building and trying to discover the cause of their choice, practical issues, and demonstrate their capabilities and introduce key attributes. The lack of knowledge of architects about these issues and their backwardness compared to other disciplines related to design and maintain buildings double its importance.
The most important basic rules to choose optimization strategy are classification algorithms and find suitable one for a specific problem. Several research papers in this area are investigated and according to them, optimization algorithms are divided into three categories including evolutionary algorithms, direct search (derivatives free), and the hybrid. The findings show that evolutionary algorithms and especially genetic algorithm application are more popular than other algorithms. The most study objectives to optimize are the environmental impact, the cost of initial investment, operating costs, and comfort criteria. In these studies, the design variables are construction materials, form and orientation of the building, cast shadows, and HVAC. In addition, the number of research papers that have used this algorithm to optimize the design of the building, than the number of articles on optimizing building control, is very low.
Article Type: Original Research |
Subject:
Highperformance Architecture
Received: 2020/04/16 | Accepted: 2020/04/25 | Published: 2020/10/21
Received: 2020/04/16 | Accepted: 2020/04/25 | Published: 2020/10/21
References
1. Garber R. Optimisation stories: The impact of building information modelling on contemporary design practice. Archit Des. 2009;79(2):6-13. [Link] [DOI:10.1002/ad.842]
2. Banos R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J. Optimization methods applied to renewable and sustainable energy: A review. Renew Sustain Energy Rev. 2011;15(4):1753-66. [Link] [DOI:10.1016/j.rser.2010.12.008]
3. Wetter M, Wright J. A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Build Environ. 2004;39(8):989-99. [Link] [DOI:10.1016/j.buildenv.2004.01.022]
4. Wang L, Yan Z, Qiao Sh, Lu GM, Huang Y. Structural and morphological transformations of mesostructured titanium phosphate through hydrothermal treatment. J Colloid Interface Sci. 2007;316(2):954-61. [Link] [DOI:10.1016/j.jcis.2007.08.047]
5. Bambrook SM, Sproul AB, Jacob D. Design optimisation for a low energy home in Sydney. Energy Build. 2011;43(7):1702-11. [Link] [DOI:10.1016/j.enbuild.2011.03.013]
6. Goia F, Haase M, Perino M. Optimizing the configuration of a façade module for office buildings by means of integrated thermal and lighting simulations in a total energy perspective. Appl Energy. 2013;108:515-27. [Link] [DOI:10.1016/j.apenergy.2013.02.063]
7. Prianto E, Depecker P. Optimization of architectural design elements in tropical humid region with thermal comfort approach. Energy Build. 2003;35(3):273-80. [Link] [DOI:10.1016/S0378-7788(02)00089-0]
8. Heiselberg P, Brohus H, Hesselholt A, Rasmussen H, Seinre E, Thomas S. Application of sensitivity analysis in design of sustainable buildings. Renew Energy. 2009;34(9):2030-6. [Link] [DOI:10.1016/j.renene.2009.02.016]
9. Hasan A, Vuolle M, Sirén K. Minimisation of life cycle cost of a detached house using combined simulation and optimisation. Build Environ. 2008;43(12):2022-34. [Link] [DOI:10.1016/j.buildenv.2007.12.003]
10. Roy R, Hinduja S, Teti R. Recent advances in engineering design optimisation: Challenges and future trends. CIRP Ann. 2008;57(2):697-715. [Link] [DOI:10.1016/j.cirp.2008.09.007]
11. Attia Sh, Hamdy M, O'Brien L, Carlucci S. Computational optimisation for zero energy buildings design: Interviews results with twenty-eight international experts. Building Simulation 2013- 13th International IBPSA Conference, 2013 26-28 August, Chambéry, France. Chambery: IBPSA; 2013. [Link]
12. kelidestan.com [Internet]. Tehran: kelidestan.com; 2016 [2016 January 14]. Available from: http://www.kelidestan.com/keys/keys.php?key=641. [Persian] [Link]
13. Pilechiha P, Mahdavinejad M, Rahimian FP, Carnemolla P, Seyedzadeh S. Multi-objective optimisation framework for designing office windows: Quality of view, daylight and energy efficiency. Appl Energy. 2020;261:114356. [Link] [DOI:10.1016/j.apenergy.2019.114356]
14. Cao K, Huang B, Wang Sh, Lin H. Sustainable land use optimization using Boundary-based Fast Genetic Algorithm. Comp Environ Urban Syst. 2012;36(3):257-69. [Link] [DOI:10.1016/j.compenvurbsys.2011.08.001]
15. Adamski M. Optimization of the form of a building on an oval base. Build Environ. 2007;42(4):1632-43. [Link] [DOI:10.1016/j.buildenv.2006.02.004]
16. Marks W. Multicriteria optimisation of shape of energy-saving buildings. Build Environ. 1997;32(4):331-9. [Link] [DOI:10.1016/S0360-1323(96)00065-0]
17. D'Cruz NA, Radford AD. A multicriteria model for building performance and design. Build Environ. 1987;22(3):167-79. [Link] [DOI:10.1016/0360-1323(87)90005-9]
18. Jedrzejuk H, Marks W. Optimization of shape and functional structure of buildings as well as heat source utilisation. Partial problems solution. Build Environ. 2002;37(11):1037-43. [Link] [DOI:10.1016/S0360-1323(01)00099-3]
19. Osyczka A. Computer aided multicriterion optimization system (CAMOS). In: Eschenauer HA, Thierauf G, editors. Discretization methods and structural optimization-procedures and applications 1989. Berlin: Springer; 1989. pp. 263-70. [Link] [DOI:10.1007/978-3-642-83707-4_33]
20. Castro-Lacouture D, Sefair JA, Flórez L, Medaglia AL. Optimization model for the selection of materials using a LEED-based green building rating system in Colombia. Build Environ. 2009;44(6):1162-70. [Link] [DOI:10.1016/j.buildenv.2008.08.009]
21. Michalek J, Choudhary R, Papalambros P. Architectural layout design optimization. Eng Optim. 2002;34(5):461-84. [Link] [DOI:10.1080/03052150214016]
22. Chakrabarty BK. Computer-aided design in urban development and management-A software for integrated planning and design by optimization. Build Environ. 2007;42(1):473-94. [Link] [DOI:10.1016/j.buildenv.2005.08.010]
23. Petersen S, Svendsen S. Method for component-based economical optimisation for use in design of new low-energy buildings. Renew Energy. 2012;38(1):173-80. [Link] [DOI:10.1016/j.renene.2011.07.019]
24. Stavrakakis GM, Zervas PL, Sarimveis H, Markatos NC. Optimization of window-openings design for thermal comfort in naturally ventilated buildings. Appl Math Model. 2012;36(1):193-211. [Link] [DOI:10.1016/j.apm.2011.05.052]
25. Back T. Evolutionary algorithms in theory and practice: Evolution strategies, evolutionary programming, genetic algorithms. Oxford: Oxford University Press; 1996. [Link]
26. Ashlock D. Evolutionary computation for modeling and optimization. Berlin: Springer Science & Business Media; 2006. [Link]
27. Darwin Ch. Evolution by natural selection: The London years, 1836-42 [Internet]. Chicago: Britannica; 2020 [cited 2016 August 16]. Available from: https://www.britannica.com/science/Copley-Medal [Link]
28. Holland JH. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor: University of Michigan Press; 1975. [Link]
29. Wright J, Farmani R. The simultaneous optimization of building fabric construction, HVAC system size, and the plant control strategy. 7th International IBPSA Conference, 2001 August 13-15, Rio de Janeiro, Brazil. Berlin: Building Simulation; 2001. [Link]
30. Coley DA, Schukat S. Low-energy design: Combining computer-based optimisation and human judgement. Build Environ. 2002;37(12):1241-7. [Link] [DOI:10.1016/S0360-1323(01)00106-8]
31. Znouda E, Ghrab-Morcos N, Hadj-Alouane A. Optimization of Mediterranean building design using genetic algorithms. Energy Build. 2007;39(2):148-53. [Link] [DOI:10.1016/j.enbuild.2005.11.015]
32. Panão MJ, Gonçalves HJ, Ferrão PM. Optimization of the urban building efficiency potential for mid-latitude climates using a genetic algorithm approach. Renew Energy. 2008;33(5):887-96. [Link] [DOI:10.1016/j.renene.2007.04.014]
33. Rakha T, Nassar K. Genetic algorithms for ceiling form optimization in response to daylight levels. Renew Energy. 2011;36(9):2348-56. [Link] [DOI:10.1016/j.renene.2011.02.006]
34. Pernodet F, Lahmidi H, Michel P. Use of genetic algorithms for multicriteria optimization of building refurbishment. 11th International IBPSA Conference, 2009 July 27-30, Glasgow, Scotland. Berlin: Building Simulation; 2009. [Link]
35. Yi YK, Malkawi AM. Optimizing building form for energy performance based on hierarchical geometry relation. Autom Constr. 2009;18(6):825-33. [Link] [DOI:10.1016/j.autcon.2009.03.006]
36. Charron R, Athienitis A. The use of genetic algorithms for a net-zero energy solar home design optimisation tool. Proceedings of PLEA 2006 (23rd Conference on Passive and Low Energy Architecture), 2006 September 6-8, Geneva, Switzerland. Geneva: PLEA; 2006. [Link]
37. Tuhus-Dubrow D, Krarti M. Genetic-algorithm based approach to optimize building envelope design for residential buildings. Build Environ. 2010;45(7):1574-81. [Link] [DOI:10.1016/j.buildenv.2010.01.005]
38. 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. 2011;25(4):656-75. [Link] [DOI:10.1016/j.aei.2011.07.009]
39. Magnier L, Haghighat F. Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Build Environ. 2010;45(3):739-46. [Link] [DOI:10.1016/j.buildenv.2009.08.016]
40. Deb K. Multi-objective optimization using evolutionary algorithms. Hoboken: John Wiley & Sons; 2001. [Link]
41. Chantrelle FP, Lahmidi H, Keilholz W, El Mankibi M, Michel P. Development of a multicriteria tool for optimizing the renovation of buildings. Appl Energy. 2011;88(4):1386-94. [Link] [DOI:10.1016/j.apenergy.2010.10.002]
42. Evins R, Pointer P, Vaidyanathan R, Burgess S. A case study exploring regulated energy use in domestic buildings using design-of-experiments and multi-objective optimisation. Build Environ. 2012;54:126-36. [Link] [DOI:10.1016/j.buildenv.2012.02.012]
43. Palonen M, Hasan A, Siren K. A genetic algorithm for optimization of building envelope and HVAC system parameters. 11th International IBPSA Conference, 2009 July 27-30, Glasgow, Scotland. Berlin: Building Simulation; 2009. pp. 159-66. [Link]
44. Sambou V, Lartigue B, Monchoux F, Adj M. Thermal optimization of multilayered walls using genetic algorithms. Energy Build. 2009;41(10):1031-6. [Link] [DOI:10.1016/j.enbuild.2009.05.007]
45. Wang W, Zmeureanu R, Rivard H. Applying multi-objective genetic algorithms in green building design optimization. Build Environ. 2005;40(11):1512-25. [Link] [DOI:10.1016/j.buildenv.2004.11.017]
46. Shi X. Design optimization of insulation usage and space conditioning load using energy simulation and genetic algorithm. Energy. 2011;36(3):1659-67. [Link] [DOI:10.1016/j.energy.2010.12.064]
47. Caldas LG, Norford LK. A design optimization tool based on a genetic algorithm. Autom Constr. 2002;11(2):173-84. [Link] [DOI:10.1016/S0926-5805(00)00096-0]
48. Lee JH. Optimization of indoor climate conditioning with passive and active methods using GA and CFD. Build Environ. 2007;42(9):3333-40. [Link] [DOI:10.1016/j.buildenv.2006.08.029]
49. Fogel LJ. Intelligence through simulated evolution: Forty years of evolutionary programming. Hoboken: Wiley; 1999. [Link]
50. Sette S, Boullart L. Genetic programming: principles and applications. Eng Appl Artif Intell. 2001;14(6):727-36. [Link] [DOI:10.1016/S0952-1976(02)00013-1]
51. Fong KF, Hanby VI, Chow TT. HVAC system optimization for energy management by evolutionary programming. Energy Build. 2006;38(3):220-31. [Link] [DOI:10.1016/j.enbuild.2005.05.008]
52. Alvarez L. Design optimization based on genetic programming. Bradford: University of Bradford; 2000. [Link]
53. Kim K, Shan Y, Nguyen XH, McKay RI. Probabilistic model building in genetic programming: A critical review. Genet Program Evol Mach. 2014;15(2):115-67. [Link] [DOI:10.1007/s10710-013-9205-x]
54. Gholami MM, Ross BJ. Passive solar building design using genetic programming. Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014 July 12, Vancouver, Canada. New York: Association for Computing Machinery; 2014. pp. 1111-8. [Link] [DOI:10.1145/2576768.2598211]
55. Iruthayarajan MW, Baskar S. Evolutionary algorithms based design of multivariable PID controller. Expert Syst Appl. 2009;36(5):9159-67. [Link] [DOI:10.1016/j.eswa.2008.12.033]
56. Hansen N, Ostermeier A. Adapting arbitrary normal mutation distributions in evolution strategies: The covariance matrix adaptation. Proceedings of IEEE International Conference on Evolutionary Computation, 1996 May 20-22, Nagoya, Japan. Piscataway: IEEE; 1996. [Link]
57. Kämpf JH, Robinson D. A hybrid CMA-ES and HDE optimisation algorithm with application to solar energy potential. Appl Soft Comput. 2009;9(2):738-45. [Link] [DOI:10.1016/j.asoc.2008.09.009]
58. Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim. 1997;11(4):341-59. [Link] [DOI:10.1023/A:1008202821328]
59. Kennedy J, Eberhart R. Particle swarm optimization. Proceedings of ICNN'95-International Conference on Neural Networks, 1995 November 27-1 December, Perth, Australia. Piscataway: IEEE; 1995. [Link]
60. Delgarm N, Sajadi B, Kowsary F, Delgarm S. Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO). Appl Energy. 2016;170:293-303. [Link] [DOI:10.1016/j.apenergy.2016.02.141]
61. Kennedy J. Particle swarm optimization. Sammut C, Webb GI. Boston: Springer; 2010. [Link]
62. Ferrara M, Sirombo E, Monti A, Fabrizio E, Filippi M. Influence of envelope design in the optimization of the operational energy costs of a multi-family building. Energy Procedia. 2016;101:216-23. [Link] [DOI:10.1016/j.egypro.2016.11.028]
63. Rapone G, Saro O. Optimisation of curtain wall façades for office buildings by means of PSO algorithm. Energy Build. 2012;45:189-96. [Link] [DOI:10.1016/j.enbuild.2011.11.003]
64. Geem ZW, Kim JH, Loganathan GV. A new heuristic optimization algorithm: Harmony search. Simulation. 2001;76(2):60-8. [Link] [DOI:10.1177/003754970107600201]
65. Yang X. Harmony search as a metaheuristic algorithm. In: Geem Z, editor. Music-inspired harmony search algorithm: Theory and applications. Berlin: Springer; 2009. pp. 1-14. [Link] [DOI:10.1007/978-3-642-00185-7_1]
66. Fesanghary M, Mahdavi M, Minary-Jolandan M, Alizadeh Y. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems. Comput Methods Appl Mech Eng. 2008;197(33-40):3080-91. [Link] [DOI:10.1016/j.cma.2008.02.006]
67. Moh'd Alia O, Mandava R, Aziz ME. A hybrid harmony search algorithm for MRI brain segmentation. Evolut Intell. 2011;4(1):31-49. [Link] [DOI:10.1007/s12065-011-0048-1]
68. Dorigo M, Blum C. Ant colony optimization theory: A survey. Theor Comput Sci. 2005;344(2-3):243-78. [Link] [DOI:10.1016/j.tcs.2005.05.020]
69. Angus D, Hendtlass T. Dynamic ant colony optimisation. Appl Intell. 2005;23(1):33-8. [Link] [DOI:10.1007/s10489-005-2370-8]
70. Dorigo M, Maniezzo V, Colorni A. Ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst Man Cybern Part B Cybern. 1996;26(1):29-41. [Link] [DOI:10.1109/3477.484436]
71. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. Science. 1983;220(4598):671-80. [Link] [DOI:10.1126/science.220.4598.671]
72. Černý V. Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. J Optim Theory Appl. 1985;45(1):41-51. [Link] [DOI:10.1007/BF00940812]
73. Hwang CR. Simulated annealing: Theory and applications. Acta Applicandae Mathematicae. 1988;12(1):108-11. [Link]
74. Kolda TG, Lewis RM, Torczon V. Optimization by direct search: New perspectives on some classical and modern methods. SIAM Rev. 2003;45(3):385-482. [Link] [DOI:10.1137/S003614450242889]
75. Torczon V. PDS: Direct search methods for unconstrained optimization on either sequential or parallel machines. Houston: Rice University; 1992. [Link] [DOI:10.21236/ADA455473]
76. Hooke R, Jeeves TA. "Direct Search" solution of numerical and statistical problems. J ACM. 1961;8(2):212-29. [Link] [DOI:10.1145/321062.321069]
77. Lewis RM, Torczon V, Trosset MW. Direct search methods: Then and now. J Comput Appl Math. 2000;124(1-2):191-207. [Link] [DOI:10.1016/S0377-0427(00)00423-4]
78. Peippo K, Lund PD, Vartiainen E. Multivariate optimization of design trade-offs for solar low energy buildings. Energy Build. 1999;29(2):189-205. [Link] [DOI:10.1016/S0378-7788(98)00055-3]
79. Noyes J, Weisstein EW. Linear Programming [Internet]. New York: Wolfram MathWorld; 2016 [cited 2016 August 17]. Available from: http://mathworld.wolfram.com/LinearProgramming.html [Link]
80. Nelder JA, Mead R. A simplex method for function minimization. Comput J. 1965;7(4):308-13. [Link] [DOI:10.1093/comjnl/7.4.308]
81. Gong X, Akashi Y, Sumiyoshi D. Optimization of passive design measures for residential buildings in different Chinese areas. Build Environ. 2012;58:46-57. [Link] [DOI:10.1016/j.buildenv.2012.06.014]
82. Saporito A, Day AR, Karayiannis TG, Parand F. Multi-parameter building thermal analysis using the lattice method for global optimisation. Energy Build. 2001;33(3):267-74. [Link] [DOI:10.1016/S0378-7788(00)00091-8]
83. Mitchell RA, Kaplan JL. Nonlinear constrained optimization by a nonrandom complex method. J Res Natl Bur Stand Sect C Eng Instrum. 1968;72C(4):249-58. [Link] [DOI:10.6028/jres.072C.019]
84. Bouchlaghem N. Optimising the design of building envelopes for thermal performance. Autom Constr. 2000;10(1):101-12. [Link] [DOI:10.1016/S0926-5805(99)00043-6]
85. Bouchlaghem NM, Letherman KM. Numerical optimization applied to the thermal design of buildings. Build Environ. 1990;25(2):117-24. [Link] [DOI:10.1016/0360-1323(90)90023-K]
86. Eisenhower B, Fonoberov V, Mezic I. Uncertainty-weighted meta-model optimization in building energy models. Proceedings of 1st Building Simulation And Optimization Conference, 2012 September 10-11, Loughborough, UK. Loughborough: IBPSA England; 2012. [Link]
87. Juan YK, Gao P, Wang J. A hybrid decision support system for sustainable office building renovation and energy performance improvement. Energy Build. 2010;42(3):290-7. [Link] [DOI:10.1016/j.enbuild.2009.09.006]
88. Hamdy M, Hasan A, Siren K. Combination of optimization algorithms for a multi-objective building design problem. IBPSA: 11th International Building Performance Simulation Association Conference, 2007 July 27-30, Glasgow, United Kingdom. Glasgow: IBPSA; 2007. [Link]
89. Pyeongchan I, Krarti M. Design optimization of energy efficient residential buildings in Tunisia. Build Environ. 2012;58:81-90. [Link] [DOI:10.1016/j.buildenv.2012.06.012]
90. Kämpf JH, Wetter M, Robinson D. A comparison of global optimization algorithms with standard benchmark functions and real-world applications using EnergyPlus. J Build Perform Simul. 2010;3(2):103-20. [Link] [DOI:10.1080/19401490903494597]
91. Anderson R, Christensen C, Horowitz S. Program design analysis using BEopt building energy optimization software: Defining a technology pathway leading to new homes with zero peak cooling demand [Report]. Golden: National Renewable Energy Laboratory; 2006 August. Report No.: NREL/CP-550-39821. Contract No.: DE-AC36-99-GO10337. [Link]
92. Yuan S, Wang Sh, Tian N. Swarm intelligence optimization and its application in geophysical data inversion. Appl Geophys. 2009;6(2):166-4. [Link] [DOI:10.1007/s11770-009-0018-x]
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |