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

  • Judyta M. Cichocka
  • Agata Migalska
  • Will N. Browne
  • Edgar Rodriguez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 724)

Abstract

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.

Keywords

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

References

  1. 1.
    Vierlinger, R.: Multi Objective Design Interface. Technischen Universitat, Wien (2013)Google Scholar
  2. 2.
    Michalewicz, Z., Fogel, B.D.: How to Solve It: Modern Heuristics, 2nd edn. Springer, Berlin (2004)CrossRefMATHGoogle Scholar
  3. 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 CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 5.
    Brownlee, J.: Clever Algorithms. Nature-Inspired Programming Recipes, First, LuLu (2011)Google Scholar
  6. 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 CrossRefGoogle Scholar
  7. 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. 8.
    Rutten, D.: Evolutionary principles applied to problem solving. Adv. Archit. Geom. (2010)Google Scholar
  9. 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 CrossRefGoogle Scholar
  10. 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. 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. 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. 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. 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 MathSciNetGoogle Scholar
  15. 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:5290209Google Scholar
  16. 16.
    Geanakoplos, J.D., Gray, L.: When Seeing Further Is Not Seeing Better n.d. (1991)Google Scholar
  17. 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)CrossRefGoogle Scholar
  18. 18.
    Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Congratulation Evolution Computer, pp. 69–73 (1998)Google Scholar
  19. 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. 20.
    Carlisle, A., Dozier, G.: An off-the-shelf PSO. Proc. Part. Swarm Optim. Work 1, 1–6 (2001)Google Scholar
  21. 21.
    Martyn, D.: Rhino grasshopper. AEC Mag. 42 (2009)Google Scholar
  22. 22.
    Webb, M.: Organic embrance. Archit. Rev. 222, 74–77 (2007)Google Scholar
  23. 23.
    Toyo Ito & Associates.: Meiso no Mori Crematorium Gifu, Japan Toyo Ito & Associates Meiso no Mori Crematorium Gifu, Japan Toyo Ito & Associates n.dGoogle Scholar
  24. 24.
    Municipal Funeral Hall in Kakamigahara, Detail, 48,786–90 (2008)Google Scholar
  25. 25.
    Sasaki, M.: Flux Structure. Toto Publishers, Tokyo (2005)Google Scholar
  26. 26.
    Sakamoto, T.: Ferre A. From control to design: parametric/algorithmic architecture. In: ACTAR (2008)Google Scholar
  27. 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. 28.
    Pugnale, A.: Computational Morphogenesis-with Karamba, Galapagos—Test on the Crematorium of Kakamigahara—Meiso no mori—Toyo Ito (2013) https://albertopugnale.wordpress.com/2013/03/25/computational-morphogenesis-with-karambagalapagos-test-on-the-crematorium-of-kakamigahara-meiso-no-mori-toyo-ito/. Accessed 30 Nov 2015
  29. 29.
    Preisinger, C.: Linking structure and parametric geometry. Archit. Des. 83, 110–113 (2013). doi:10.1002/ad.1564 Google Scholar
  30. 30.
    Preisinger, C.: Karamba User Manual (version 1.1.0) (2015)Google Scholar
  31. 31.
    Pugnale, A.: Engineering Architecture. Advances of a technological practice, Ph.D. Thesis. Politecnico di Torino (2009). doi:10.1017/CBO9781107415324.004
  32. 32.
    Cichocka, J.M.: Particle Swarm Optimization for Architectural Design. Silvereye 1.0, Code of Space, Vienna (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Faculty of ArchitectureWroclaw University of TechnologyWrocławPoland
  2. 2.Faculty of ElectronicsWroclaw University of TechnologyWrocławPoland
  3. 3.School Engineering and Computer ScienceVictoria University of WellingtonWellingtonNew Zealand
  4. 4.School of Architecture and DesignVictoria University of WellingtonWellingtonNew Zealand

Personalised recommendations