Advertisement

HYDRA Distributed Multi-Objective Optimization for Designers

  • Marcin KosickiEmail author
  • Marios Tsiliakos
  • Martha Tsigkari
Conference paper
  • 520 Downloads

Abstract

Architectural design problems can be quite involved, as there is a plethora of – usually conflicting – criteria that one has to address in order to find an optimal, performative solution. Multi-Objective Optimization (MOO) techniques can thus prove very useful, as they provide solution spaces which can traverse the different trade-offs of convoluted design options. Nevertheless, they are not widely used as (a) they are computationally expensive and (b) the resulting solution space can be proven difficult to visualize and navigate, particularly when dealing with higher dimensional spaces. This paper will present a system, which merges bespoke multi-objective optimization with a parametric CAD system, enhanced by supercomputing, into a single, coherent workflow, in order to address the above issues. The system architecture ensures optimal use of existing compute resources and enables massive performance speed-up, allowing for fast review and delivery cycles. The application aims to provide architects, designers and engineers with a better understanding of the design space, aiding the decision-making process by procuring tangible data from different objectives and finally providing fit (and sometimes unforeseen) solutions to a design problem. This is primarily achieved by a graphical interface of easy to navigate solution spaces of design options, derived from their respective Pareto fronts, in the form of a web-based interactive dashboard. Since understanding high-dimensionality data is a difficult task, multivariate analysis techniques were implemented to post-process the data before displaying it to end users. Visual Data Mining (VDM) and Machine Learning (ML) techniques were incorporated to facilitate knowledge discovery and exploration of large sets of design options at an early design stage. The system is demonstrated and assessed on an applied design case study of a master-planning project, where the benefits of the process are more evident, especially due to its complexity and size.

Keywords

Distributed computing Parallel computing Performance design Optimization Evolutionary computing High Performance Computing 

References

  1. Branke, J., Schmeck, H., Deb, K., Reddy, S.M.: Parallelizing multi-objective evolutionary algorithms: cone separation. In: Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), pp. 1952–1957. IEEE (2005).  https://doi.org/10.1109/CEC.2004.1331135
  2. Brown, N., Tseranidis, S., Mueller, C.: Multi-objective optimization for diversity and performance in conceptual structural design. In: Proceedings of the International Association for Shell and Spatial Structures (IASS), Future Visions, Amsterdam, The Netherlands, 17–20 August 2015 (2015). http://digitalstructures.mit.edu/files/2015-09/ncb-iass-paper-final.pdf
  3. Chaszar, A., von Buelow, P., Turrin, M.: Multivariate interactive visualization of data in generative design. In: Ramtin, A., Chronis, A., Hanna, S., Turrin, M. (eds.), SimAUD, London (2016)Google Scholar
  4. Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011).  https://doi.org/10.1016/j.advengsoft.2011.05.014CrossRefGoogle Scholar
  5. Fielding, R.T.: Architectural styles and the design of network-based software architecturese. University of California, Irvine (2000). http://www.ics.uci.edu/~fielding/pubs/dissertation/rest_arch_style.htm
  6. Harding, J.: Dimensionality reduction for parametric design exploration. In: Adriaenssens, S., Gramazio, F., Kohler, M., Menges, A., Pauly, M. (eds.), Advances in Architectural Geometry 2016, pp. 204–221. vdf Hochschulverlag AG, Zurich, Switzerland (2016).  https://doi.org/10.3218/3778-4_19
  7. Keough, I., Benjamin, D.: Multi-objective optimization in architectural design. In: Proceedings of the 2010 Spring Simulation Multiconference. Orlando, Florida, USA (2010)Google Scholar
  8. Kicinger, R., Arciszewski, T., DeJong, K.: Evolutionary design of steel structures in tall buildings. J. Comput. Civ. Eng. 19(3), 223–238 (2005). https://doi.org/10.1061/(ASCE)0887-3801(2005)19:3(223).
  9. Kyropoulou, M., Ferrer, P., Subramaniam, S.: 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. Hong Kong (2018). https://www.researchgate.net/publication/329718843_Optimization_of_Intensive_Daylight_Simulations_A_Cloud-based_Methodology_using_HPC_High_Performance_Computing
  10. Mueller, C., Ochsendorf, J.: An integrated computational approach for creative conceptual structural design. In: Proceedings of the International Association for Shell and Spatial Structures (IASS) Symposium 2013, pp. 1–6 (2013)Google Scholar
  11. Mueller, V.: Second Generation Prototype of a Design Performance Optimization Framework, April 2015 (2015)Google Scholar
  12. Müller, P., et al.: Procedural modeling of buildings. ACM Trans. Graph. 25(3), 614 (2006). https://doi.org/10.1145/1141911.1141931
  13. Newman, S.: Building Microservices, 1st edn. O’Reilly Media, Sebastopol (2015)Google Scholar
  14. Newton, D.: Multi-objective qualitative optimization (MOQO) in architectural design. In: Kepczynska-Walczak, A., Bialkowski, S. (eds.), Computing for a Better Tomorrow - Proceedings of the 36th eCAADe Conference, vol. 1, pp. 187–196, Lodz, Poland (2018)Google Scholar
  15. Parish, Y.I.H., Müller, P.: Procedural modeling of cities. In: 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 301–308, August 2001.  https://doi.org/10.1145/383259.383292
  16. Prusinkiewicz, P., Lindenmayer, A.: The Algorithmic Beauty of Plants. Springer, Heidelberg (1991)Google Scholar
  17. Roudsari, M.S., Pak, M., Smith, A.: Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. In: 13th Conference of International Building Performance Simulation Association, pp. 3129–3135 (2013). http://www.ibpsa.org/proceedings/bs2013/p_2499.pdf
  18. Roudsari, M., Yi, Y., Drew, C.: Applying climate-based daylight modelling (CBDM) for a macro scale master plan design case study: the Great City in China. In: ASim. Shanghai, China (2012). https://www.ibpsa.org/proceedings/asim2012/0097.pdf
  19. Rutten, D.: Galapagos: on the logic and limitations of generic solvers. Archit. Des. 83(2), 132–135 (2013)Google Scholar
  20. Sileryte, R., D’Aquilio, A., Di Stefano, D., Yang, D., Turrin, M.: Supporting exploration of design alternatives using multivariate analysis algorithms. In: Ramtin, A., Chronis, A., Hanna, S., Turrin, M. (eds.), Proceedings of the Symposium on Simulation for Architecture and Urban Design, pp. 215–222, London, UK (2016)Google Scholar
  21. Talbi, E.G., Mostaghim, S., Okabe, T., Ishibuchi, H., Rudolph, G., Coello Coello, C.A.: Parallel approaches for multiobjective optimization. In: Multiobjective Optimization. LNCS, vol. 5252, pp. 349–372 (2008).  https://doi.org/10.1007/978-3-540-88908-3-13
  22. Thinkbox. Deadline. Accessed 14 Apr 14 2019. https://deadline.thinkboxsoftware.com
  23. Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 144–173 (2003).  https://doi.org/10.1109/TEVC.2003.810751CrossRefGoogle Scholar
  24. Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Trans. Neural Netw. 11(3), 586–600 (2000).  https://doi.org/10.1109/72.846731CrossRefGoogle Scholar
  25. Vierlinger, R.: A framework for flexible search and optimization in parametric design. In: Rethinking Prototyping - Proceedings of the Design Modelling Symposium, October 2013 (2013).  https://doi.org/10.13140/RG.2.1.1516.8727

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Marcin Kosicki
    • 1
    Email author
  • Marios Tsiliakos
    • 1
  • Martha Tsigkari
    • 1
  1. 1.Foster + PartnersLondonUK

Personalised recommendations