Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-06T12:08:14.374Z Has data issue: false hasContentIssue false
  • English
  • Français

Optimization test of a rule-based swarm intelligence simulation for the conceptual design process

Published online by Cambridge University Press:  15 July 2020

Asli Agirbas Open the ORCID record for Asli Agirbas [Opens in a new window]
Asli Agirbas*
Affiliation:
Fatih Sultan Mehmet Vakif University, Sutluce Mah. Karaagac Cad. No: 12/A Beyoglu, Istanbul, Turkey
*
Author for correspondence: Asli Agirbas, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Today, in the field of architecture, bio-inspired algorithms can be used to design and seek solutions to design problems. Two of the most popular algorithms are the genetic algorithm (GA) and swarm intelligence algorithm. However, no study has examined the simultaneous use of these two bio-inspired algorithms in the field of architecture. Therefore, this study aims to test whether these two bio-inspired algorithms can work together. To this end, GA is used in this study to optimize the rule-based swarm algorithm for the conceptual design process. In this optimization test, the objective was to increase the surface area, and the constraints are parcel boundary and building height. Further, the forms associated with swarm agents were determined as variables. Following the case studies, the study concludes that the two bio-inspired algorithms can effectively work together.

Keywords

Bio-inspired designconceptual design modelgenetic algorithmoptimizationswarm intelligence
Type
Research Article
Information
AI EDAM , Volume 34 , Issue 4 , November 2020 , pp. 477 - 491
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

Ab Rashid, M, Tiwari, A and Hutabarat, W (2019) Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing. doi:10.1017/S0890060419000131CrossRefGoogle Scholar
Adeniran, A and El Ferik, S (2017) A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 31, 327–358.CrossRefGoogle Scholar
Agirbas, A (2018) Performance-based design optimization for minimal surface based form. Architectural Science Review 61, 384–399.CrossRefGoogle Scholar
Ahlquist, S, Erb, D and Menges, A (2015) Evolutionary structural and spatial adaptation of topologically differentiated tensile systems in architectural design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29, 393–415.CrossRefGoogle Scholar
Alaliyat, S, Yndestad, H and Sanfilippo, F (2014) Optimisation of boids swarm model based on genetic algorithm and particle swarm optimisation algorithm (comparative study). Proceedings of 28th European Conference on Modelling and Simulation (ECMS), Brescia, Italy, May 27–30.CrossRefGoogle Scholar
Attia, S, Gratia, E, De Herde, A and Hensen, JL (2012) Simulation-based decision support tool for early stages of zero-energy building design. Energy and Buildings 49, 2–15.CrossRefGoogle Scholar
Attia, S, Hamdy, M, O'Brien, W and Carlucci, S (2013) Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design. Energy and Buildings 60, 110–124.CrossRefGoogle Scholar
Bentley, PJ and Wakefield, JP (1997) Conceptual evolutionary design by a genetic algorithm. Engineering Design and Automation 3, 119–132.Google Scholar
Benvenuti, S, Ceccanti, F and De Kestelier, X (2013) Living on the moon: topological optimization of a 3D-printed lunar shelter. Nexus Network Journal 15, 285–302.CrossRefGoogle Scholar
Benyus, JM (1998) Biomimicry: Innovation Inspired by Nature. New York: Harper Collins Publishers.Google Scholar
Bonabeau, E, Dorigo, M and Theraulaz, G (1999) Swarm Intelligence. From Natural to Artificial Systems. New York: Oxford University Press.CrossRefGoogle Scholar
Camazine, S (1991) Self-organizing pattern-formation on the combs of honeybee colonies. Behavioral Ecology and Sociobiology 28, 61–76.CrossRefGoogle Scholar
Camazine, S, Deneubourg, JL, Franks, NR, Sneyd, J, Theraulaz, G and Bonabeau, E (2001) Self-Organization in Biological Systems. New Jersey: Princeton University Press.CrossRefGoogle Scholar
Cerver tools (2018) Locust – Behavioral Animation Tools. Available at https://www.grasshopper3d.com/group/cervertools (accessed 31 March 2018).Google Scholar
Chakrabarti, A and Shu, L (2010) Biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 453–454.Google Scholar
Chakrabarti, A, Sarkar, P, Leelavathamma, B and Nataraju, B (2005) A functional representation for aiding biomimetic and artificial inspiration of new ideas. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 19, 113–132.CrossRefGoogle Scholar
Chatzi, EN and Koumousis, VK (2009) Optimal inelastic design of multistorey reinforced concrete buildings towards uniform distribution of minimal damage. International Conference on Structural Engineering Dynamics, Ericeira, Portugal, June 22–24.Google Scholar
Chen, Y-W, Kobayashi, K, Huang, X and Nakao, Z (2006) Genetic algorithms for optimization of boids model. In Gabrys, B, Howlett, RJ and Jain, LC (eds), Knowledge-Based Intelligent Information and Engineering Systems. Berlin: Springer, pp. 55–62.CrossRefGoogle Scholar
Cui, Z and Shi, Z (2009) Boid particle swarm optimisation. International Journal of Innovative Computing and Applications 2, 77–85.CrossRefGoogle Scholar
DeLanda, M (2002) Deleuze and the use of the genetic algorithm in architecture. Architectural Design 71, 9–12.Google Scholar
Do, E (2002) Drawing marks, acts, and reacts: toward a computational sketching interface for architectural design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 16, 149–171.CrossRefGoogle Scholar
Dorigo, M and Stutzle, T (2004) Ant Colony Optimization. Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
Dorigo, M, Bonabeau, E and Theraulaz, G (2000) Ant algorithms and stigmergy. Future Generation Computer Systems 16, 851–871.CrossRefGoogle Scholar
Eberhart, RC and Kennedy, J (1995) A new optimizer using particle swarm theory. Proceedings of Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, October 4–6, pp. 39–43.CrossRefGoogle Scholar
Eberhart, RC, Dobbins, RW and Simpson, PK (1996) Computational Intelligence PC Tools. Boston: Academic Press.Google Scholar
Elbeltagi, E, Hegazy, T and Grierson, D (2005) Comparison among five evolutionary-based optimization algorithms. Advanced Engineering Informatics 19, 43–53.CrossRefGoogle Scholar
Evins, R (2013) A review of computational optimization methods applied to sustainable building design. Renewable and Sustainable Energy Reviews 22, 230–245.CrossRefGoogle Scholar
Felkner, J, Chatzi, E and Kotnik, T (2013) Interactive particle swarm optimization for the architectural design of truss structures. Proceedings of the 2013 IEEE Symposium Computational Intelligence for Engineering Solutions (CIES), Singapore, April 16–19, pp. 15–22.CrossRefGoogle Scholar
Fourie, PC and Groenwold, AA (2002) The particle swarm optimization algorithm in size and shape optimization. Structural and Multidisciplinary Optimization 23, 259–267.CrossRefGoogle Scholar
Frazer, JH (1995) An Evolutionary Architecture. London: AA Publications.Google Scholar
Garner, S (1992) The undervalued role of drawing in design. In Thistlewood, D (ed), Drawing Research and Development. London: Longman, pp. 98–109.Google Scholar
Goel, V (1992) Ill-structured representations for Ill-structured problems. Proceedings of the Fourteenth Annual Conference of the Cognitive Science Society, Bloomington, Indiana, July 29–August 1, pp. 130–135.Google Scholar
Goel, V (1995) Sketches of Thought. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Goldberg, DE and Holland, JH (1988) Genetic algorithms and machine learning. Machine Learning 3, 95–99.CrossRefGoogle Scholar
González, J and Fiorito, F (2015) Daylight design of office buildings: optimisation of external solar shadings by using combined simulation methods. Buildings 5, 560–580.CrossRefGoogle Scholar
Hamdy, M, Palonen, M and Hasan, A (2012) Implementation of pareto-archive NSGA-II algorithms to a nearly-zero-energy building optimisation problem. Proceedings of the First Building Simulation and Optimization Conference, Loughborough, UK, September 10–11, pp.181–188.Google Scholar
Hofmeyer, H and Davila Delgado, J (2015) Coevolutionary and genetic algorithm based building spatial and structural design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29, 351–370.CrossRefGoogle Scholar
Hu, Y-J, Wang, Y, Wang, Z-L, Wang, Y-Q and Zhang, BC (2014) Machining scheme selection based on a new discrete particle swarm optimization and analytic hierarchy process. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 28, 71–82.CrossRefGoogle Scholar
Jin, Y and Benami, O (2010) Creative patterns and stimulation in conceptual design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 191–209.CrossRefGoogle Scholar
Jo, JH and Gero, JS (2006) Space layout planning using an evolutionary approach. Artificial Intelligence in Engineering 12, 149–162.CrossRefGoogle Scholar
Kämpf, J, Wetter, M and Robinson, D (2010) A comparison of global optimization algorithms with standard benchmark functions and real-world applications using EnergyPlus. Journal of Building Performance Simulation 3, 103–120.CrossRefGoogle Scholar
Kennedy, J and Eberhart, R (1995) Particle swarm optimization. Proceedings of ICNN’39 – International Conference on Neural Networks, Perth, Australia, November 27–December 1, pp. 1942–1948.CrossRefGoogle Scholar
Kim, S (2013) Interval estimation of construction cost using case-based reasoning and genetic algorithms. Journal of Asian Architecture and Building Engineering 11, 327–334.CrossRefGoogle Scholar
Kramar, D, Cica, D, Sredanovic, B and Kopac, J (2015) Design of fuzzy expert system for predicting of surface roughness in high-pressure jet assisted turning using bioinspired algorithms. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 96–106.CrossRefGoogle Scholar
Lee, DT and Schachter, BJ (1980) Two algorithms for constructing a Delaunay triangulation. International Journal of Computer Information Sciences 9, 219–242.CrossRefGoogle Scholar
Lipson, H and Shpitalni, M (2000) Conceptual design and analysis by sketching. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 14, 391–401.CrossRefGoogle Scholar
Machairas, V, Tsangrassoulis, A and Axarli, K (2014) Algorithms for optimization of building design: a review. Renewable and Sustainable Energy Reviews 31, 101–112.CrossRefGoogle Scholar
Mukerjee, A, Agrawal, R, Tiwari, N and Hasan, N (1997) Qualitative sketch optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 11, 311–323.CrossRefGoogle Scholar
Nguyen, A, Reiter, S and Rigo, P (2014) A review on simulation-based optimization methods applied to building performance analysis. Applied Energy 113, 1043–1058.CrossRefGoogle Scholar
Partridge, BL (1982) The structure and function of fish schools. Scientific American 246, 114–123.CrossRefGoogle ScholarPubMed
Pawlyn, M (2011) Biomimicry in Architecture. London: RIBA Publishing.Google Scholar
Pedersen Zari, M (2015) Ecosystem processes for biomimetic architectural and urban design. Architectural Science Review 58, 106–119.CrossRefGoogle Scholar
Rajan, S (1995) Sizing, shape, and topology design optimization of trusses using genetic algorithm. Journal of Structural Engineering 121, 1480–1487.CrossRefGoogle Scholar
Rasheed, K and Hirsh, H (1999) Learning to be selective in genetic-algorithm-based design optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 13, 157–169.CrossRefGoogle Scholar
Rawat, CD, Shahani, A, Natu, N, Badami, A and Hingorani, R (2012) A genetic algorithm for VLSI floor planning. International Journal of Engineering Science and Advanced Technology 2, 412–415.Google Scholar
Reddy, MJ and Kumar, DN (2007) An efficient multi-objective optimization algorithm based on swarm intelligence for engineering design. Engineering Optimization 39, 49–68.CrossRefGoogle Scholar
Renner, G and Ekárt, A (2003) Genetic algorithms in computer aided design. Computer Aided Design 35, 709–726.CrossRefGoogle Scholar
Reynolds, CW (1987) Flocks, herds, and schools: a distributed behavioral model. Computer Graphics 21, 25–34.CrossRefGoogle Scholar
Reynolds, C. (2017) Boids: Background and Update. Available at http://www.red3d.com/cwr/boids/ (accessed 18 August 2017).Google Scholar
Rutten, D (2013) Galapagos: on the logic and limitations of generic solvers. Architectural Design 83, 132–135.CrossRefGoogle Scholar
Sartori, J, Pal, U and Chakrabarti, A (2010) A methodology for supporting “transfer” in biomimetic design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 483–506.CrossRefGoogle Scholar
Schon, DA (1983) The Reflective Practitioner: How Professionals Think in Action. New York: Basic Books.Google Scholar
Schon, DA and Wiggins, G (1992) Kinds of seeing and their functions in designing. Design Studies 13, 135–156.CrossRefGoogle Scholar
Shi, X, Tian, Z, Chen, W, Si, B and Jin, X (2016) A review on building energy efficient design optimization from the perspective of architects. Renewable and Sustainable Energy Reviews 65, 872–884.CrossRefGoogle 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 29, 457–469.CrossRefGoogle Scholar
Suwa, M and Tversky, B (1996) What architects see in their sketches: implications for design tools. In Proceedings of CHI’96, Vancouver, BC, Canada, April 13–18, pp. 191–192.CrossRefGoogle Scholar
Tedeschi, A (2014) AAD_Algorithms-Aided Design: Parametric Strategies Using Grasshopper. Brienza: Le Penseur Publisher.Google Scholar
Theraulaz, G and Bonabeau, F (1995) Modelling the collective building of complex architectures in social insects with lattice swarms. Journal of Theoretical Biology 177, 381–400.CrossRefGoogle Scholar
Theraulaz, G, Gautrais, J, Camazine, S and Deneubourg, J (2003) The formation of spatial patterns in social insects: from simple behaviours to complex structures. Philosophical Transactions: Mathematical, Physical and Engineering Sciences 361, 1263–1282.CrossRefGoogle ScholarPubMed
Tuhus-Dubrow, D and Krarti, M (2009) Comparative analysis of optimization approaches to design building envelope for residential buildings. ASHRAE Transactions 115, 554.Google Scholar
van Embden Andres, MV, Turrin, M and von Buelow, P (2011) Architectural DNA: a genetic exploration of complex structures. International Journal of Architectural Computing 9, 133–149.CrossRefGoogle Scholar
Vattam, S, Helms, M and Goel, A (2010) A content account of creative analogies in biologically inspired design. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 24, 467–481.CrossRefGoogle Scholar
Vincent, J, Bogatyreva, O, Bogatyrev, N, Bowyer, N and Pahl, K (2006) Biomimetics: its practice and theory. Journal of the Royal Society Interface 3, 471–482.CrossRefGoogle ScholarPubMed
Wang, W, Zmeureanu, R and Rivard, H (2005) Applying multi-objective genetic algorithms in green building design optimization. Building and Environment 40, 1512–1525.CrossRefGoogle Scholar
Wetter, M and Wright, J (2004) A comparison of deterministic and probabilistic optimization algorithms for non-smooth simulation-based optimization. Building and Environment 39, 989–999.CrossRefGoogle Scholar
Wright, J and Alajmi, A (2005) The robustness of genetic algorithms in solving unconstrained building optimization problems. Proceedings of the Ninth International IBPSA Conference, Montreal, Canada, August 15–18, pp.1361–1368.Google Scholar
Yu, S, Austern, G, Jirathiyut, T and Moral, M (2014) Climatic formations: evolutionary dynamics of urban morphologies. Journal of Asian Architecture and Building Engineering 13, 317–324.CrossRefGoogle Scholar
Yu, R, Gu, N, Ostwald, M and Gero, J (2015) Empirical support for problem–solution coevolution in a parametric design environment. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 29, 33–44.CrossRefGoogle Scholar