Comparative Study of BSO and GA for the Optimizing Energy in Ambient Intelligence

  • Wendoly J. Gpe. Romero-Rodríguez
  • Victor Manuel Zamudio Rodríguez
  • Rosario Baltazar Flores
  • Marco Aurelio Sotelo-Figueroa
  • Jorge Alberto Soria Alcaraz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7095)

Abstract

One of the concerns of humanity today is developing strategies for saving energy, because we need to reduce energetic costs and promote economical, political and environmental sustainability. As we have mentioned before, in recent times one of the main priorities is energy management. The goal in this project is to develop a system that will be able to find optimal configurations in energy savings through management light. In this paper a comparison between Genetic Algorithms (GA) and Bee Swarm Optimization (BSO) is made. These two strategies are focus on lights management, as the main scenario, and taking into account the activity of the users, size of area, quantity of lights, and power. It was found that the GA provides an optimal configuration (according to the user’s needs), and this result was consistent with Wilcoxon’s Test.

Keywords

Ambient Intelligence Energy Management GA BSO 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zelkha, E., Epstein, B.B.: From Devices to Ambient Intelligence: The Transformation of Consumer Electronics. In: Digital Living Room Conference (1998)Google Scholar
  2. 2.
    ISTAG Scenarios for Ambient Intelligence in Compiled by Ducatel, K., M.B. 2010 (2011) Google Scholar
  3. 3.
    Sulaiman, F., Ahmad, A.: Automated Fuzzy Logic Light Balanced Control Algorithm Implemented in Passive Optical Fiber Daylighting System (2006)Google Scholar
  4. 4.
    Boman, M., Davidsson, P., Skarmeas, N., Clark, K.: Energy saving and added customer value in intelligent buildings. In: Third International Conference on the Practical Application of Intelligent Agents and Multi-Agent Technology (1998)Google Scholar
  5. 5.
    Akkermans, J., Ygge, F.: Homebots: Intelligent decentralized services for energy management. Ergon Verlag (1996)Google Scholar
  6. 6.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. University of Michigan Press (1975)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks (1995)Google Scholar
  8. 8.
    Pham, D., Ghanbarzadeh, A., Koc, E., Otri, S., Rahim, S.: The bees algorithm–a novel tool for complex optimisation problems. In: Proc 2nd Int Virtual Conf. on Intelligent Production Machines and Systems (IPROMS 2006), pp. 454–459 (2006)Google Scholar
  9. 9.
    Nieto, J.: Algoritmos basados en cúmulos de partículas para la resolución de problemas complejos (2006)Google Scholar
  10. 10.
    Sotelo-Figueroa, M.A., Baltazar, R., Carpio, M.: Application of the Bee Swarm Optimization BSO to the Knapsack Problem. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Soft Computing for Recognition Based on Biometrics. SCI, vol. 312, pp. 191–206. Springer, Heidelberg (2010), doi:10.1007/978-3-642-15111-8_12 ISBN: 978-3-642-15110-1CrossRefGoogle Scholar
  11. 11.
    Sotelo-Figueroa, M.A., del Rosario Baltazar-Flores, M., Carpio, J.M., Zamudio, V.: A Comparation between Bee Swarm Optimization and Greedy Algorithm for the Knapsack Problem with Bee Reallocation. In: 2010 Ninth Mexican International Conference on Artificial Intelligence (MICAI), November 8-13, pp. 22–27 (2010), doi: 10.1109/MICAI.2010.32 Google Scholar
  12. 12.
    Sotelo-Figueroa, M., Baltazar, R., Carpio, M.: Application of the Bee Swarm Optimization BSO to the Knapsack Problem. Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS) 5 (2011)Google Scholar
  13. 13.
    Haupt, R.L.: Practical Genetic Algorithms (2004)Google Scholar
  14. 14.
    Hernández, J. L. (s.f.): Web de Tecnología Eléctrica. Obtenido de Web de Tecnología Eléctrica, http://www.tuveras.com/index.html
  15. 15.
    Fernandez, J.G. (s.f.): EDISON, Aprendizaje Basado en Internet. Obtenido de EDISON, Aprendizaje Basado en Internet, http://edison.upc.edu/
  16. 16.
    Woolson, R.: Wilcoxon Signed-Rank Test. Wiley Online Library (1998)Google Scholar
  17. 17.
    Laszlo, C.: Lighting Design & Asoc. (n.d.). Manual de luminotecnia para interiores. retrieved from Manual de luminotecnia para interiores, http://www.laszlo.com.ar/manual.htm
  18. 18.
    Sotelo-Figueroa, M.A.: Aplicacion de Metahueristicas en el Knapsack Problem (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Wendoly J. Gpe. Romero-Rodríguez
    • 1
  • Victor Manuel Zamudio Rodríguez
    • 1
  • Rosario Baltazar Flores
    • 1
  • Marco Aurelio Sotelo-Figueroa
    • 1
  • Jorge Alberto Soria Alcaraz
    • 1
  1. 1.Division of Research and Postgraduate StudiesLeon Insitute of TechnologyLeónMéxico

Personalised recommendations