Optimizing Costs and Quality of Interior Lighting by Genetic Algorithm

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.

[1]  Jason Livingston,et al.  Designing With Light: The Art, Science and Practice of Architectural Lighting Design , 2014 .

[2]  M. Hanan,et al.  On Steiner’s Problem with Rectilinear Distance , 1966 .

[3]  Rüdiger Dillmann,et al.  Real-Time Smoke and Bleeding Simulation in Virtual Surgery , 2007, MMVR.

[4]  Enrico Zio,et al.  Multiobjective optimization of the inspection intervals of a nuclear safety system: A clustering-based framework for reducing the Pareto Front ☆ , 2010 .

[5]  Panos Y. Papalambros,et al.  Principles of Optimal Design: Author Index , 2000 .

[6]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[7]  Labanca Nicola,et al.  Energy Efficiency Status Report 2012 - Electricity Consumption and Efficiency Trends in the EU-27 , 2012 .

[8]  Siân Kleindienst,et al.  Interactive expert support for early stage full-year daylighting design: a user’s perspective on Lightsolve , 2013 .

[9]  Gustavo Patow,et al.  A Survey of Inverse Rendering Problems , 2003, Comput. Graph. Forum.

[10]  Feng Zhou,et al.  Refined single trunk tree: a rectilinear steiner tree generator for interconnect prediction , 2002, SLIP '02.

[11]  Carlos A. Coello Coello,et al.  Interactive Approaches Applied to Multiobjective Evolutionary Algorithms , 2013 .

[12]  Marouane Kessentini,et al.  Preference Incorporation in Evolutionary Multiobjective Optimization , 2015 .

[13]  Peter Kuster.  Interior Lighting For Designers , 2016 .

[14]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[15]  Marc Parizeau,et al.  DEAP: evolutionary algorithms made easy , 2012, J. Mach. Learn. Res..

[16]  Ertunga C. Özelkan,et al.  Optimizing complex building design for annual daylighting performance and evaluation of optimization algorithms , 2015 .

[17]  Enrico Zio,et al.  A COMPARISON OF METHODS FOR SELECTING PREFERRED SOLUTIONS IN MULTIOBJECTIVE DECISION MAKING , 2012 .

[18]  Alice Plebe,et al.  Particle physics and polyedra proximity calculation for hazard simulations in large-scale industrial plants , 2016 .

[19]  Zbigniew Michalewicz,et al.  An Experimental Comparison of Binary and Floating Point Representations in Genetic Algorithms , 1991, ICGA.

[20]  Brian J. Ross,et al.  Interior Illumination Design Using Genetic Programming , 2015, EvoMUSART.

[21]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[22]  Kevin G. Suffern,et al.  Painting with light , 2002, SIGGRAPH '02.

[23]  Cengiz Kahraman,et al.  Computational Intelligence Systems in Industrial Engineering , 2012, Atlantis Computational Intelligence Systems.

[24]  G. Ross.  Inverse Source Problems in Optics , 1979 .

[25]  A. Kahng,et al.  On optimal interconnections for VLSI , 1994 .

[26]  Paola Sansoni,et al.  Sustainable Indoor Lighting , 2015 .

[27]  Vincenzo Cutello,et al.  Evolving Illumination Design Following Genetic Strategies , 2017, IJCCI.

[28]  Marilyne Andersen,et al.  A generative facade design method based on daylighting performance goals , 2012 .

[29]  Cengiz Kahraman.  Computational Intelligence Systems in Industrial Engineering: With Recent Theory and Applications , 2012 .

[30]  Dana S. Richards,et al.  Steiner tree problems , 1992, Networks.

[31]  Hans Jürgen Prömel,et al.  The Steiner Tree Problem , 2002 .

[32]  Michael F. Cohen,et al.  Radioptimization: goal based rendering , 1993, SIGGRAPH.

[33]  Marouane Kessentini,et al.  Chapter Four - Preference Incorporation in Evolutionary Multiobjective Optimization: A Survey of the State-of-the-Art , 2015, Adv. Comput..

[34]  G. W. Larson,et al.  Rendering with radiance - the art and science of lighting visualization , 2004, Morgan Kaufmann series in computer graphics and geometric modeling.