Abstract
This paper proposes the use of multi-objective optimization to help in the design of interior lighting. The optimization provides an approximation of the inverse lighting problem, the determination of potential light sources satisfying a set of given illumination requirements, for which there are no analytic solutions in real instances. In order to find acceptable solutions we use the metaphor of genetic evolution, where individuals are lists of possible light sources, their positions and lighting levels. We group the many, and often not explicit, requirements for a good lighting, into two competing groups, pertaining to the quality and the costs of a lighting solution. The cost group includes both energy consumption and the electrical wiring required for the light installation. Objectives inside each group are blended with weights, and the two groups are treated as multi-objectives. The architectural space to be lighted is reproduced with 3D graphic software Blender, used to simulate the effect of illumination. The final Pareto set resulting from the genetic algorithm is further processed with clustering, in order to extract a very small set of candidate solutions, to be evaluated by the architect.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gordon, G.: Interior Lighting for Designers. Wiley, New York (2014)
Livingston, J.: Designing With Light: The Art, Science, and Practice of Architectural Lighting Design. Wiley, New York (2015)
Wunderlich, C.H.: Light and economy: an essay about the economy of prehistoric and ancient lamps. In: Chranovski, L. (ed.) Lychnological News, pp. 251–264. LychnoServices, Hauterive (Suisse) (2003)
Kahraman, C. (ed.): Computational Intelligence Systems in Industrial Engineering With Recent Theory and Applications. Atlantis Press, Paris (2012)
Commercial buildings energy consumption survey: Technical report, U.S. Energy Information Administration (2012)
Bertoldi, P., Hirl, B., Labanca, N.: Energy efficiency status report. Technical report, European Commission—Institute for Energy and Transport (2012)
Sansoni, P., Farini, A., Mercatelli, L. (eds.): Sustainable Indoor Lighting. Springer, Berlin (2015)
Jaimes, A.L., Coello, C.A.C.: Interactive approaches applied to multiobjective evolutionary algorithms. In: Doumpos, M., Grigoroudis, E. (eds.) Multicriteria Decision Aid and Artificial Intelligence: Theory and Applications, pp. 191–207. Wiley, New York (2013)
Bechikh, S., Kessentini, M., Said, L.B., Ghédira, K.: Preference incorporation in evolutionary multiobjective optimization: a survey of the state-of-the-art. Adv. Comput. 98, 141–207 (2015)
Plebe, A., Cutello, V., Pavone, M.: Evolving illumination design following genetic strategies. In Sabourin, C., Merelo, J.J., Warwick, K., Madani, K., O’Reilly, U.M. (eds.) 9th International Joint Conference on Computational Intelligence, pp. 222–233. Scitepress (2017)
Larson, G.W., Shakespeare, R.: Rendering with Radiance: The Art and Science of Lighting Visualization. Morgan Kaufmann, San Francisco, CA (1997)
Baltes, H. (ed.): Inverse Source Problems in Optics. Princeton University Press, Princeton, NJ (1978)
Kawai, J., Painter, J.S., Cohen, M.F.: Radioptimization: goal based rendering. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, pp. 147–154 (1993)
Schoeneman, C., Dorsey, J., Smits, B., Arvo, J., Greenberg, D.: Painting with light. In: Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques, pp. 143–146 (1993)
Patow, G., Pueyo, X.: A survey of inverse rendering problems. Comput. Graph. Forum 22, 663–687 (2003)
Papalambros, P.Y., Wilde, D.J.: Principles of Optimal Design. Cambridge University Press, Cambridge, UK (1988)
Andersen, M., Gagne, J.M., Kleindienst, S.: Interactive expert support for early stage full-year daylighting design: a user’s perspective on Lightsolve. Autom. Constr. 35, 338–352 (2013)
Gagne, J., Andersen, M.: A generative facade design method based on daylighting performance goals. J. Build. Perform. Simul. 5, 141–154 (2012)
Futrell, B., Ozelkan, E.C., Brentrup, D.: Optimizing complex building design for annual daylighting performance and evaluation of optimization algorithms. Energy Build. 92, 234–245 (2014)
Moylan, K., Ross, B.J.: Interior illumination design using genetic programming. In Johnson, C., Carballal, A., ao Correia, J. (eds.) Proceedings IV Conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design, pp. 148–160 (2015)
Daenzer, S., Montgomery, K., Dillmann, R., Unterhinninghofen, R.: Real-time smoke and bleeding simulation in virtual surgery. In: Westwood, J.D., Haluck, R.S., Hoffman, H.M., Mogel, G.T., Phillips, R., Robb, R.A., Vosburgh, K.G. (eds.) Medicine Meets Virtual Reality, pp. 94–99. IOS Press, Amsterdam (2007)
Plebe, A., Grasso, G.: Particle physics and polyedra proximity calculation for hazard simulations in large-scale industrial plants. In: American Institute of Physics Conference Proceedings, pp. 090003–1–090003–4 (2016)
Janikow, C.Z., Michalewicz, Z.: An experimental comparison of binary and floating point representations in genetic algorithms. In: Proceedings of the 4th International Conference on Genetic Algorithms, pp. 31–36 (1991)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International Conference on Parallel Problem Solving From Nature, pp. 849–858 (2000)
Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagné, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171–2175 (2012)
Hwang, F.K., Richards, D.S., Winter, P.: The Steiner Tree Problem. North Holland, Amsterdam (1992)
Hanan, M.: On Steiner’s problem with rectilinear distance. SIAM J. Appl. Math. 14, 255–265 (1966)
Kahng, A.B., Robins, G.: On Optimal Interconnections for VLSI. Springer, Berlin (1994)
Chen, H., Qiao, C., Zhou, F., Cheng, C.K.: Refined single trunk tree: a rectilinear Steiner tree generator for interconnect prediction. In: Proceedings of the International Workshop on System-Level Interconnect Prediction, pp. 85–89 (2002)
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, New York (2001)
Zio, E., Bazzo, R.: Multiobjective optimization of the inspection intervals of a nuclear safety system: a clustering-based framework for reducing the pareto front. Ann. Nucl. Energy 37, 798–812 (2010)
Chiu, S.L.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994)
Zio, E., Bazzo, R.: A comparison of methods for selecting preferred solutions in multiobjective decision making (4), 23–43
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Plebe, A., Cutello, V., Pavone, M. (2019). Optimizing Costs and Quality of Interior Lighting by Genetic Algorithm. In: Sabourin, C., Merelo, J.J., Madani, K., Warwick, K. (eds) Computational Intelligence. IJCCI 2017. Studies in Computational Intelligence, vol 829. Springer, Cham. https://doi.org/10.1007/978-3-030-16469-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-16469-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-16468-3
Online ISBN: 978-3-030-16469-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)