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Multi-objective Genetic Algorithm for Interior Lighting Design

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Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

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Abstract

This paper proposes a novel system to help in the design of interior lighting. It is based on multi-objective optimization of the key criteria involved in lighting design: the respect of a given target level of illuminance, uniformity of lighting, and electrical energy saving. The proposed solution integrates the 3D graphic software Blender, used to reproduce the architectural space and to simulate the effect of illumination, and the genetic algorithm NSGA-II. This solution offers advantages in design flexibility over previous related works.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, University of Catania, V.le A. Doria 6, 95125, Catania, Italy

    Alice Plebe & Mario Pavone

Authors
  1. Alice Plebe
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  2. Mario Pavone
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Corresponding author

Correspondence to Alice Plebe .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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Plebe, A., Pavone, M. (2018). Multi-objective Genetic Algorithm for Interior Lighting Design. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_19

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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