Hostname: page-component-cd9895bd7-8ctnn Total loading time: 0 Render date: 2024-12-26T22:11:36.207Z Has data issue: false hasContentIssue false
  • English
  • Français

SSIEA: a hybrid evolutionary algorithm for supporting conceptual architectural design

Published online by Cambridge University Press:  15 July 2020

Likai Wang Open the ORCID record for Likai Wang [Opens in a new window] ,
Patrick Janssen  and
Guohua Ji
Likai Wang
Affiliation:
School of Architecture and Urban Planning, Nanjing University, Nanjing, Jiangsu, China
Patrick Janssen
Affiliation:
Department of Architecture, National University of Singapore, Singapore
Guohua Ji*
Affiliation:
School of Architecture and Urban Planning, Nanjing University, Nanjing, Jiangsu, China
*
Author for correspondence: Guohua Ji, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Significant research has been undertaken focusing on the application of evolutionary algorithms for design exploration at conceptual design stages. However, standard evolutionary algorithms are typically not well-suited to supporting such optimization-based design exploration due to the lack of design diversity in the optimization result and the poor search efficiency in discovering high-performing design solutions. In order to address the two weaknesses, this paper proposes a hybrid evolutionary algorithm, called steady-stage island evolutionary algorithm (SSIEA). The implementation of SSIEA integrates an island model approach and a steady-state replacement strategy with an evolutionary algorithm. The combination aims to produce optimization results with rich design diversity while achieving significant fitness progress in a reasonable amount of time. Moreover, the use of the island model approach allows for an implicit clustering of the design population during the optimization process, which helps architects explore different alternative design directions. The performance of SSIEA is compared against other optimization algorithms using two case studies. The result shows that, in contrast to the other algorithms, SSIEA is capable of achieving a good compromise between design diversity and search efficiency. The case studies also demonstrate how SSIEA can support conceptual design exploration. For architects, the optimization results with diverse and high-performing solutions stimulate richer reflection and ideation, rendering SSIEA a helpful tool for conceptual design exploration.

Keywords

Conceptual architectural designevolutionary algorithmsisland modelsoptimization-based explorationsteady-state replacement strategies
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 4 , November 2020 , pp. 458 - 476
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Agapie, A and Wright, AH (2014) Theoretical analysis of steady state genetic algorithms. Applications of Mathematics 59, 509–525. https://doi.org/10.1007/s10492-014-0069-z.CrossRefGoogle Scholar
Alba, E and Troya, JM (1999) A survey of parallel distributed genetic algorithms. Complexity 4, 31–52. https://doi.org/10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.3.CO;2-W.3.0.CO;2-4>CrossRefGoogle Scholar
Bradner, E, Iorio, F and Davis, M (2014) Parameters tell the design story: Ideation and abstraction in design optimization. In 2014 Proceedings of the Symposium on Simulation for Architecture and Urban Design, Tampa, FL, USA: Society for Computer Simulation International, pp. 172–197.Google Scholar
Cao, K, Huang, B, Wang, S and Lin, H (2012) Sustainable land use optimization using boundary-based fast genetic algorithm. Computers, Environment and Urban Systems 36, 257–269. https://doi.org/10.1016/j.compenvurbsys.2011.08.001.CrossRefGoogle Scholar
Chen, KW (2015) Architectural Design Exploration of Low-Exergy (LowEx) Buildings in the Tropics. ETH Zurich. Zurich, Switzerland. https://doi.org/10.3929/ethz-a-010613319CrossRefGoogle Scholar
Chipperfield, AJ and Fleming, PJ (1994) Parallel Genetic Algorithms: A Survey. Research Report. ACSE Research Report 518. Department of Automatic Control and Systems Engineering. Sheffield, UK.Google Scholar
Cichocka, J, Browne, WN and Ramirez, ER (2017) Optimization in the architectural practice: An international survey. In Proceedings of the 22nd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2017), pp. 387–397, Suzhou, China, 5-8 April 2017.Google Scholar
Dino, IG (2012) Creative design exploration by parametric generative systems in architecture. METU Journal of the Faculty of Architecture 29, 207–224. https://doi.org/10.4305/METU.JFA.2012.1.12.Google Scholar
Ekici, B, Cubukcuoglu, C, Turrin, M and Sariyildiz, IS (2018) Performative computational architecture using swarm and evolutionary optimisation: A review. Building and Environment 147, 356–371. https://doi.org/10.1016/j.buildenv.2018.10.023.CrossRefGoogle Scholar
Horii, H, Miki, M, Koizumi, T and Tsujiuchi, N (2002) Asynchronous migration of island parallel GA for multi-objective optimization problem. In Asia-Pacific Conference on Simulated Evolution and Learning (SEAL 2002). pp. 86–90, Singapore, 18-22 Nov, 2002.Google Scholar
Jakubiec, JA and Reinhart, CF (2011) DIVA 2.0: Integrating daylight and thermal simulations using Rhinoceros 3D, Daysim and EnergyPlus. In Proceedings of Building Simulation 2011. Sydney, Australia, International Building Performance Simulation Association, pp. 2202–2209.Google Scholar
Janssen, P (2005) A Design Method and Computational Architecture for Generating and Evolving Building Designs. The Hong Kong Polytechnic University, Hong Kong, China.Google Scholar
Kyropoulou, M, Ferrer, P and Subramaniam, S (2018) Optimization of intensive daylight simulations: A cloud-based methodology using HPC (high performance computing). In PLEA 2018 HONG KONG Smart and Healthy Within the 2-degree Limit (PLEA 2018). Hong Kong, China, pp. 150–155.Google Scholar
Maaranen, H, Miettinen, K and Penttinen, A (2007) On initial populations of a genetic algorithm for continuous optimization problems. Journal of Global Optimization 37, https://doi.org/10.1007/s10898-006-9056-6.Google Scholar
Makki, M, Showkatbakhsh, M, Tabony, A and Weistock, M (2018) Evolutionary algorithms for generating urban morphology: Variations and multiple objectives. International Journal of Architectural Computing. (Figure 1), 1–31. https://doi.org/10.1177/1478077118777236.Google Scholar
Montgomery, J and Chen, S (2012) A simple strategy for maintaining diversity and reducing crowding in differential evolution. In 2012 IEEE Congress on Evolutionary Computation, CEC 2012, (June 2014). https://doi.org/10.1109/CEC.2012.6252891.Google Scholar
Natanian, J, Aleksandrowicz, O and Auer, T (2019) A parametric approach to optimizing urban form, energy balance and environmental quality: The case of Mediterranean districts. Applied Energy 254, 113637. https://doi.org/10.1016/j.apenergy.2019.113637.CrossRefGoogle Scholar
Nault, E, Waibel, C, Carmeliet, J and Andersen, M (2018) Development and test application of the UrbanSOLve decision-support prototype for early-stage neighborhood design. Building and Environment 137, 58–72. https://doi.org/10.1016/j.buildenv.2018.03.033.CrossRefGoogle Scholar
Nguyen, A-T, Reiter, S and Rigo, P (2014) A review on simulation-based optimization methods applied to building performance analysis. Applied Energy 113, 1043–1058. https://doi.org/10.1016/j.apenergy.2013.08.061.Google Scholar
Park, G-B, Jeong, M and Choi, D-H (2015) A guideline for parameter setting of an evolutionary algorithm using optimal latin hypercube design and statistical analysis. International Journal of Precision Engineering and Manufacturing 16, 2167–2178. https://doi.org/10.1007/s12541-015-0279-7.Google Scholar
Rechenraum GmbH (2019) Goat. Retrieved from: https://www.rechenraum.com/en/goat.htmlGoogle Scholar
Rodrigues, E, Gaspar, AR and Gomes, Á (2013) An evolutionary strategy enhanced with a local search technique for the space allocation problem in architecture, Part 1: Methodology. Computer Aided Design 45, 898–910. https://doi.org/10.1016/j.cad.2013.01.003.CrossRefGoogle Scholar
Rodrigues, E, Fernandes, MS, Gomes, Á, Rodrigues, A and Costa, JJ (2019) Performance-based design of multi-story buildings for a sustainable urban environment: A case study. Renewable and Sustainable Energy Reviews 113, 109243. https://doi.org/10.1016/j.rser.2019.109243.CrossRefGoogle Scholar
Scheibehenne, B, Greifeneder, R and Todd, PM (2010) Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research 37, 409–425. https://doi.org/10.1086/651235.CrossRefGoogle Scholar
Shields, MD and Zhang, J (2016) The generalization of Latin hypercube sampling. Reliability Engineering and System Safety 148, 96–108. https://doi.org/10.1016/j.ress.2015.12.002.Google Scholar
Si, B, Tian, Z, Jin, X, Zhou, X and Shi, X (2019) Ineffectiveness of optimization algorithms in building energy optimization and possible causes. Renewable Energy 134, 1295–1306. https://doi.org/10.1016/j.renene.2018.09.057.Google Scholar
Su, Z and Yan, W (2015) A fast genetic algorithm for solving architectural design optimization problems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AI EDAM 29, 457–469. https://doi.org/10.1017/S089006041500044X.Google Scholar
Talbi, EG (2009) Metaheuristics: From Design to Implementation, Vol. 136. https://doi.org/10.1002/9780470496916.CrossRefGoogle Scholar
Thierens, D (2002) Adaptive mutation rate control schemes in genetic algorithms. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600), Vol. 1. IEEE, Honolulu, HI, USA, 2002, pp. 980-985 vol.1, https://doi.org/10.1109/CEC.2002.1007058.CrossRefGoogle Scholar
Touloupaki, E and Theodosiou, T (2017) Performance simulation integrated in parametric 3D modeling as a method for early stage design optimization – A review. Energies 10, https://doi.org/10.3390/en10050637.Google Scholar
Toutou, A, Fikry, M and Mohamed, W (2018) The parametric based optimization framework daylighting and energy performance in residential buildings in hot arid zone. Alexandria Engineering Journal 57, 3595–3608. https://doi.org/10.1016/j.aej.2018.04.006.CrossRefGoogle Scholar
Turrin, M, Von Buelow, P and Stouffs, R (2011) Design explorations of performance driven geometry in architectural design using parametric modeling and genetic algorithms. Advanced Engineering Informatics 25, 656–675. https://doi.org/10.1016/j.aei.2011.07.009.CrossRefGoogle Scholar
Vierlinger, R (2013) Multi Objective Design Interface. https://doi.org/10.13140/RG.2.1.3401.0324.Google Scholar
Vierlinger, R (2019) OCTOPUS. Retrieved from https://www.food4rhino.com/app/octopusGoogle Scholar
Waibel, C, Wortmann, T, Evins, R and Carmeliet, J (2019) Building energy optimization: An extensive benchmark of global search algorithms. Energy and Buildings 187, 218–240. https://doi.org/10.1016/j.enbuild.2019.01.048.CrossRefGoogle Scholar
Wang, L, Janssen, P and Ji, G (2018 a) Efficiency versus Effectiveness: A Study on Constraint Handling for Architectural Evolutionary Design. In Proceedings of the 23rd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2018) Vol. 1 Beijing, China, 17-19 May 2018, pp. 163–172.Google Scholar
Wang, L, Janssen, P and Ji, G (2018 b) Utility of evolutionary design in architectural form finding: An investigation into constraint handling strategies. In International Conference on Design Computing and Cognition. Cham: Springer, pp. 177–194. https://doi.org/10.1007/978-3-030-05363-5_10.Google Scholar
Wang, L, Janssen, P and Ji, G (2019 a) Diversity and efficiency: A hybrid evolutionary algorithm combining the Island model with a steady-state replacement strategy. In Proceedings of the 23rd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2019) Vol. 2, Wellington, New Zealand, 15-18 April 2019, pp. 593–602.Google Scholar
Wang, L, Janssen, P, Chen, KW, Tong, Z and Ji, G (2019 b) Subtractive building massing for performance-based architectural design exploration: A case study of daylighting optimization. Sustainability (Switzerland) 11, 6965. https://doi.org/10.3390/su11246965.CrossRefGoogle Scholar
Whitley, D, Rana, S and Heckendorn, RB (1999) The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7, 33–47.Google Scholar
Wolpert, DH and Macready, WG (1997) No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82. https://doi.org/10.1109/4235.585893.CrossRefGoogle Scholar
Woodbury, RF and Burrow, AL (2006) Whither design space? AIE EDAM: Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 63–82. https://doi.org/10.10170S0890060406060057.Google Scholar
Wortmann, T (2017) Model-based optimization for architectural design: Optimizing daylight and glare in Grasshopper. Technology | Architecture + Design 1448, https://doi.org/10.1080/24751448.2017.1354615.Google Scholar
Wortmann, T (2018) Efficient, Visual, and Interactive Architectural Design Optimization with Model-based Methods. Singagore University of Technology and Design. https://doi.org/10.13140/RG.2.2.15380.55685.Google Scholar
Wortmann, T and Nannicini, G (2017) Introduction to architectural design optimization. In Karakitsiou, A, Migdalas, A, Rassia, ST and Pardalos, PM (eds), City Networks: Collaboration and Planning for Health and Sustainability. Cham: Springer International Publishing, pp. 259–278. https://doi.org/10.1007/978-3-319-65338-9_14.CrossRefGoogle Scholar
Wortmann, T and Schroepfer, T (2019) From optimization to performance-informed design. In 2019 Proceedings of the Symposium on Simulation for Architecture and Urban Design (SimAUD 2019). Vol. 51. Atlanta, GA, USA, pp. 261–268.Google Scholar
Wortmann, T, Waibel, C, Nannicini, G, Evins, R, Schroepfer, T and Carmeliet, J (2017) Are genetic algorithms really the best choice for building energy optimization? In 2017 Proceedings of the Symposium on Simulation for Architecture and Urban Design. Toronto, Canada: Society for Computer Simulation International, pp. 51–58.Google Scholar
Yousif, S and Yan, W (2018) Clustering forms for enhancing architectural design optimization. In Proceedings of the 23rd International Conference for Computer-Aided Architectural Design Research in Asia (CAADRIA 2018) Vol. 2, pp. 431–440, Beijing, China, 17-19 May 2018.Google Scholar