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Cluster Computing

, Volume 22, Supplement 2, pp 3479–3486 | Cite as

Optimal design of high-rise building wiring based on ant colony optimization

  • Chunjiang LiuEmail author
Article
  • 100 Downloads

Abstract

With the continuous acceleration of the building process of urban modernization in China, the scale of buildings continues to expand, and more and more information equipment and electrical equipment are applied to high-rise buildings, which brings new challenges to the design of electricity for high-rise buildings. In order to ensure the normal operation of electric equipment and choose the reasonable electricity consumption and reduce the cost of investment and maintenance, it is necessary to discuss the wiring optimization problems of high-rise buildings. Therefore, this paper chooses the ant colony optimization to optimize the wiring design of the electrical equipment of the building intelligently. Based on the biological model of ant colony optimization, the traditional ant colony optimization is improved, and the ant colony optimization is proposed in the continuous space optimization problem. It optimizes the routing path of high-rise buildings by simulating the shortest path of the ant colony to find food. The computing results of power parameters show that, the improved ant colony optimization model proposed in this paper can shorten the length of high-rise building wiring, the control of voltage drop, line loss and other power parameters can improve the economic benefit of the algorithm, and it is feasible in the optimization of high-rise building wiring.

Keywords

Ant colony optimization High-rise building Wiring optimization Load calculation 

References

  1. 1.
    Sun, K., Li, S., Chu, S., et al.: The optimum design of high-rise building structure based on the strength and stiffness of genetic algorithm. In: International Conference in Swarm Intelligence. Springer International Publishing, pp. 50–57 (2015)Google Scholar
  2. 2.
    Ge, S., Branch, S.: Discussion on the optimization strategy of HVAC design for high-rise building. Shanxi Archit. 32(5), 120–123 (2017)Google Scholar
  3. 3.
    Yuan, C., Chengguo, W.U., Zhang, Y., et al.: The optimization model of concrete-filled square steel tubular columns based on accelerating genetic algorithm. J. North China Univ. Water Resour. Electr. Power 2, 10 (2016)Google Scholar
  4. 4.
    Sabri, N.A.M., Basari, A.S.H., Hussin, B., et al.: Dijkstra-ant colony optimization algorithm for shortest and safest evacuation in high rise building. J. Technol. 79(3), 69–77 (2017)Google Scholar
  5. 5.
    Choi, S.W., Ji, H.S., Hong, M.L., et al.: Wind-induced response control model for high-rise buildings based on resizing method. J. Civil Eng. Manag. 21(2), 239–247 (2015)Google Scholar
  6. 6.
    Jiao, K., Wu, G., Jia, S., et al.: Discussion on optimization design method of high-rise concrete structures. Build. Struct. 54(4), 15–18 (2016)Google Scholar
  7. 7.
    Atia, D.Y., Ruta, D., Poon, K., et al.: Cost effective, scalable design of indoor distributed antenna systems based on particle swarm optimization and prufer strings. In: Evolutionary Computation. IEEE, pp. 4159–4166 (2016)Google Scholar
  8. 8.
    Qin, J., Dai, C., Liu, B., et al.: Optimization of CFETR CSMC cabling based on numerical modeling and experiments. Supercond. Sci. Technol. 28(12), 125008 (2015)Google Scholar
  9. 9.
    Lin, Q.: Study on computer-aided optimized design of structured cabling construction drawing of intelligent building. In: International Conference on Management, Education, Information and Control (2015)Google Scholar
  10. 10.
    Nilsson, E., Bermudez, S.I., Ballarino, A., et al.: Design optimization of the Nb3Sn 11 T dipole for the high luminosity LHC. IEEE Trans. Appl. Supercond. 27, 1–5 (2017)Google Scholar
  11. 11.
    Stancari, G., Previtali, V., Valishev, A., et al.: Conceptual design of hollow electron lenses for beam halo control in the Large Hadron Collider. IEEE Trans. Nucl. Sci. 61(4), 1552–1557 (2015)Google Scholar
  12. 12.
    Zhou, M., Ben-Tzvi, P.: Design and optimization of a five-finger haptic glove mechanism. J. Mech. Robot. 7, 041008 (2015)Google Scholar
  13. 13.
    Fleiter, J., Ballarino, A., Bonasia, A., et al.: Optimization of Nb3Sn rutherford cables geometry for the high luminosity LHC. IEEE Trans. Appl. Supercond. 27, 1–5 (2017)Google Scholar
  14. 14.
    Barzi, E., Zlobin, A.V.: Research and development of Nb3Sn wires and cables for high-field accelerator magnets. IEEE Trans. Nucl. Sci. 63(2), 783–803 (2015)Google Scholar
  15. 15.
    Zlobin, A.V., Andreev, N., Apollinari, G., et al.: Development and test of a single-aperture 11 T, demonstrator dipole for LHC upgrades. IEEE Trans. Appl. Supercond. 23(3), 4000904 (2015)Google Scholar
  16. 16.
    Agnese, R., Page, W.A., Bunker, R., et al.: Improved WIMP-search reach of the CDMS II germanium data. Phys. Rev. D 92(7), 072003 (2015)Google Scholar
  17. 17.
    Ilin, K., Yagotintsev, K.A., Zhou, C., et al.: Experiments and FE modeling of stress–strain state in ReBCO tape under tensile, torsional and transverse load. Supercond. Sci. Technol. 28(5), 263–269 (2015)Google Scholar
  18. 18.
    Kim, K., Oh, S., Park, J.S., et al.: Conceptual design study of the K-DEMO magnet system. Fusion Eng. Des. 96–97, 281–285 (2015)Google Scholar
  19. 19.
    Liu, N., Plets, D., Vanhecke, K., et al.: Wireless indoor network planning for advanced exposure and installation cost minimization. Eurasip J. Wirel. Commun. Netw. 2015(1), 1–14 (2015)Google Scholar
  20. 20.
    Schlinker, B., Mysore, R.N., Smith, S., et al.: Condor: better topologies through declarative design. ACM SIGCOMM Comput. Commun. Rev. 45(5), 449–463 (2015)Google Scholar
  21. 21.
    Bose, R., Sarddar, D.: A new approach in mobile gaming on cloud-based architecture using Citrix and VMware technologies. Braz. J. Sci. Technol. 2(1), 1–13 (2015)Google Scholar
  22. 22.
    Qin, J., Dai, C., Wang, Q., et al.: Stress–strain distribution analysis in Bi2212 subcable based on numerical modeling and experiment. IEEE Trans. Appl. Supercond. 26(6), 1–7 (2016)Google Scholar
  23. 23.
    Jahani, A., Feghhi, J., Makhdoum, M.F., et al.: Optimized forest degradation model (OFDM): an environmental decision support system for environmental impact assessment using an artificial neural network. J. Environ. Plan. Manage. 59(2), 222–244 (2016)Google Scholar
  24. 24.
    Ma, Y.: Development of high-performance iron-based superconducting wires and tapes. Physica C 516(3), 17–26 (2015)Google Scholar
  25. 25.
    Zola, E., Kassler, A.J., Kim, W.: Joint user association and energy aware routing for green small cell mmWave Backhaul networks. In: Wireless Communications and Networking Conference. IEEE, pp. 1–6 (2017)Google Scholar
  26. 26.
    Lenoir, G., Aubin, V.: Mechanical characterization and modeling of a powder-in-tube MgB2 strand. IEEE Trans. Appl. Supercond. 27, 1–5 (2016)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Economics and ManagementChang’an UniversityXi’anChina

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