Skip to main content

SILVEREYE – The Implementation of Particle Swarm Optimization Algorithm in a Design Optimization Tool

Part of the Communications in Computer and Information Science book series (CCIS,volume 724)


Engineers and architects are now turning to use computational aids in order to analyze and solve complex design problems. Most of these problems can be handled by techniques that exploit Evolutionary Computation (EC). However existing EC techniques are slow [8] and hard to understand, thus disengaging the user. Swarm Intelligence (SI) relies on social interaction, of which humans have a natural understanding, as opposed to the more abstract concept of evolutionary change. The main aim of this research is to introduce a new solver Silvereye, which implements Particle Swarm Optimization (PSO) in the Grasshopper framework, as the algorithm is hypothesized to be fast and intuitive. The second objective is to test if SI is able to solve complex design problems faster than EC-based solvers. Experimental results on a complex, single-objective high-dimensional benchmark problem of roof geometry optimization provide statistically significant evidence of computational inexpensiveness of the introduced tool.


  • Architectural Design Optimization (ADO)
  • Particle Swarm Optimization (PSO)
  • Swarm Intelligence (SI)
  • Evolutionary Computation (EC)
  • Structural Optimization

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. 1.

    E = 3100 [kN/cm²], G = 1291.67 [kN/cm²], gamma = 25 [kN/m³], alphaT = 1.0E−5[1/C°], fy = 1.67 [kN/cm²].


  1. Vierlinger, R.: Multi Objective Design Interface. Technischen Universitat, Wien (2013)

    Google Scholar 

  2. Michalewicz, Z., Fogel, B.D.: How to Solve It: Modern Heuristics, 2nd edn. Springer, Berlin (2004)

    CrossRef  MATH  Google Scholar 

  3. Wortmann, T., Costa, A., Nannicini, G., Schroepfer, T.: Advantages of surrogate models for architectural design optimization. Artif. Intell. Eng. Des. Anal. Manuf. 29, 471–481 (2015). doi:10.1017/S0890060415000451

    CrossRef  Google Scholar 

  4. Wetter, M., Polak, E.A.: Convergent optimization method using pattern search algorithms with adaptive precision simulation. Build. Serv. Eng. Res. Technol. 25, 327 (2004)

    CrossRef  Google Scholar 

  5. Brownlee, J.: Clever Algorithms. Nature-Inspired Programming Recipes, First, LuLu (2011)

    Google Scholar 

  6. Marques, A.I., Garcia, V., Sanchez, J.S.: A literature review on the application of evolutionary computing to credit scoring. J. Oper. Res. Soc. 64, 1384–1399 (2013). doi:10.1057/jors.2012.145

    CrossRef  Google Scholar 

  7. Rutten, D.: Navigating multi-dimensional landscapes in foggy weather as an analogy for generic problem solving. In: 16th International Conference on Geometry Graph (2014)

    Google Scholar 

  8. Rutten, D.: Evolutionary principles applied to problem solving. Adv. Archit. Geom. (2010)

    Google Scholar 

  9. Nguyen, A.T., Reiter, S.: Passive designs and strategies for low-cost housing using simulation-based optimization and different thermal comfort criteria. J. Build. Perform. Simul. 7, 68–81 (2013). doi:10.1080/19401493.2013.770067

    CrossRef  Google Scholar 

  10. Pugnale, A., Echengucia, T., Sassone, M.: Computational morphogenesis, design of freeform surfaces. In: Adriaenssens, S., Block, P., Veenendaal, D., Williams, C. (eds.), Shell Structures Architecture Form-finding Optimization, pp. 250–61. Routledge (2014)

    Google Scholar 

  11. Cichocka, J.M., Browne, W.N., Rodriguez, E.: Evolutionary optimization processes as design tools. In: Proceedings of 31th International PLEA Conference Architecture in (R)evolution, Bologna, 9–11 September (2015)

    Google Scholar 

  12. Cichocka, J.M., Browne, W.N., Rodriguez, E.: Optimization in the architectural practice—an international survey. In: Janssen, P., Loh, P., Raonic A., Schnabel M. (eds.), Flows Glitches, Proceedings of 22nd International Conference Association Computer Architecture Design. Res. Asia 2017, Paper, p. 155. The Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong (2017)

    Google Scholar 

  13. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, Proceedings, vol. 4, pp. 1942–1948 (1995). doi:10.1109/ICNN.1995.488968

  14. Zhang, Y., Wang, S., Ji, G.: A comprehensive survey on particle swarm optimization algorithm and its applications. Math. Probl. Eng. (2015). doi:10.1155/2015/931256

    MathSciNet  Google Scholar 

  15. Millonas, M.M.: Swarms, phase transitions, and collective intelligence. In: St Fe Inst Stud Sci Complexity-Proceedings, vol. 30 (1994). doi:citeulike-article-id:5290209

    Google Scholar 

  16. Geanakoplos, J.D., Gray, L.: When Seeing Further Is Not Seeing Better n.d. (1991)

    Google Scholar 

  17. Xue, B., Zhang, M., Browne, W.N.: Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43, 1656–1671 (2013)

    CrossRef  Google Scholar 

  18. Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Congratulation Evolution Computer, pp. 69–73 (1998)

    Google Scholar 

  19. Eberhart, R.C., Shi, Y.: Comparison between genetic algorithms and particle swarm optimization. In: Ep’ 1998, pp. 611–616 (1998). doi:10.1007/BFb0040812

  20. Carlisle, A., Dozier, G.: An off-the-shelf PSO. Proc. Part. Swarm Optim. Work 1, 1–6 (2001)

    Google Scholar 

  21. Martyn, D.: Rhino grasshopper. AEC Mag. 42 (2009)

    Google Scholar 

  22. Webb, M.: Organic embrance. Archit. Rev. 222, 74–77 (2007)

    Google Scholar 

  23. Toyo Ito & Associates.: Meiso no Mori Crematorium Gifu, Japan Toyo Ito & Associates Meiso no Mori Crematorium Gifu, Japan Toyo Ito & Associates n.d

    Google Scholar 

  24. Municipal Funeral Hall in Kakamigahara, Detail, 48,786–90 (2008)

    Google Scholar 

  25. Sasaki, M.: Flux Structure. Toto Publishers, Tokyo (2005)

    Google Scholar 

  26. Sakamoto, T.: Ferre A. From control to design: parametric/algorithmic architecture. In: ACTAR (2008)

    Google Scholar 

  27. Pugnale, A., Sassone, M.: Morphogenesis and structural optimization of shell structures with the aid of a genetic algorithm. J. Int. Assoc. Shell Spat. Struct. 48, 161–166 (2007)

    Google Scholar 

  28. Pugnale, A.: Computational Morphogenesis-with Karamba, Galapagos—Test on the Crematorium of Kakamigahara—Meiso no mori—Toyo Ito (2013) Accessed 30 Nov 2015

  29. Preisinger, C.: Linking structure and parametric geometry. Archit. Des. 83, 110–113 (2013). doi:10.1002/ad.1564

    Google Scholar 

  30. Preisinger, C.: Karamba User Manual (version 1.1.0) (2015)

    Google Scholar 

  31. Pugnale, A.: Engineering Architecture. Advances of a technological practice, Ph.D. Thesis. Politecnico di Torino (2009). doi:10.1017/CBO9781107415324.004

  32. Cichocka, J.M.: Particle Swarm Optimization for Architectural Design. Silvereye 1.0, Code of Space, Vienna (2016)

    Google Scholar 

Download references


This work is partially supported by the “THELXINOE: Erasmus Euro-Oceanian Smart City Network”. This is an Erasmus Mundus Action-2 Strand-2 (EMA2/S2) project funded by the European Union.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Judyta M. Cichocka .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Cichocka, J.M., Migalska, A., Browne, W.N., Rodriguez, E. (2017). SILVEREYE – The Implementation of Particle Swarm Optimization Algorithm in a Design Optimization Tool. In: Çağdaş, G., Özkar, M., Gül, L., Gürer, E. (eds) Computer-Aided Architectural Design. Future Trajectories. CAADFutures 2017. Communications in Computer and Information Science, vol 724. Springer, Singapore.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5196-8

  • Online ISBN: 978-981-10-5197-5

  • eBook Packages: Computer ScienceComputer Science (R0)