Skip to main content
SpringerLink
Log in

Elevator group-control policy based on neural network optimized by genetic algorithm

  • Published:
Transactions of Tianjin University Aims and scope Submit manuscript

Abstract

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Cao Liting, Zhang Zhaoli, Hou Jue. Dynamic optimized dispatching system for elevator group based on artificial intelligent theory[C]. In: Proceedings of International Electronic Measurement and Instrument Conference. Xi’an, China, 2007, Vol.3: 884–889.

  2. Ishikawa T, Miyauchi A, Kaneko M. Supervisory control for elevator group by using fuzzy expert system which also addresses traveling time[C]. In: Proceedings of IEEE International Industrial Technology Conference. Bangalor, India, 2000, Vol.1: 87–94.

  3. Xue Yingcheng, Pan Junming. Application real-time expert control in automatic pour system[J]. Electrical Drive Automation, 2001, 23(1): 21–23 (in Chinese).

    Google Scholar 

  4. Zhang Lei, Wang Lisong, Zang Yuwen et al. On rule checking and learning in an acupuncture diagnosis fuzzy expert system by genetic algorithm[C]. In: Proceedings of 1995 IEEE International Conference on Fuzzy Systems. Yokohama, Japan, 1995, Vol.2: 455–460.

  5. Liu Jianchang, Liu Yiyang. Ant colony algorithm and fuzzy neural network-based intelligent dispatching algorithm of an elevator group control system[C]. In: Proceedings of IEEE International Control and Automation Conference. Guangzhou, China, 2007. 2306–2310.

  6. Eguchi T, Hirasawa K, Hu Jinglu et al. Elevator group supervisory control systems using genetic network programming[ C]. In: Proceedings of Congress on Evolutionary Computation. California, USA, 2004, Vol.2: 1661–1667.

  7. Cui Naxin, Wang Ping, Zhang Qingfan. Inverter applied to reconstruct of elevator[J]. Electrical Drive Automation, 2000, 22(2): 12–14 (in Chinese).

    Google Scholar 

  8. Chen Zhimin, Luo Fei, Xu Yuge et al. Design and implementation of modern elevator group control system[C]. In: Proceedings of the Fifth International Conference on Machine Learning and Cybernetics. Dalian, China, 2006. 676–681.

  9. Lu Xingyu, He Peng, Chang Jianghua. Research of dispatching method in elevator group control system based on fuzzy neural network[J]. Computer Technology and Development, 2008, 18(1): 220–222, 225 (in Chinese).

    Google Scholar 

  10. Wan Jianru, Liu Chunjiang, Liu Hongchi. Optimal dispatch control of elevators based on a forward neural network[J]. Systems Engineering and Electronics, 2003, 25(4): 446–468 (in Chinese).

    MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Tianjin University, Tianjin, 300072, China

    Hong Shen  (沈 虹), Jianru Wan  (万健如), Yingpei Liu  (刘英培) & Guangye Li  (李光叶)

  2. Baoding Power Supply Company, Baoding, 071000, China

    Zhichao Zhang  (张志超)

Authors
  1. Hong Shen  (沈 虹)
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jianru Wan  (万健如)
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhichao Zhang  (张志超)
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yingpei Liu  (刘英培)
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guangye Li  (李光叶)
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianru Wan  (万健如).

Additional information

Supported by National Natural Science Foundation of China (No.60874077) and Specialized Research Funds for Doctoral Program of Higher Education of China (No.20060056054) and Research Funds for Scientific Financing Projects of Quality Control Public Welfare Profession (No.2007GYB172).

SHEN Hong, born in 1978, female, doctorate student.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Shen, H., Wan, J., Zhang, Z. et al. Elevator group-control policy based on neural network optimized by genetic algorithm. Trans. Tianjin Univ. 15, 245–248 (2009). https://doi.org/10.1007/s12209-009-0043-0

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12209-009-0043-0

Keywords

Search

Navigation