single-jc.php

JACIII Vol.10 No.3 pp. 385-394
doi: 10.20965/jaciii.2006.p0385
(2006)

Paper:

Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization

Toru Eguchi*, Jin Zhou*, Shinji Eto*, Kotaro Hirasawa*,
Jinglu Hu*, and Sandor Markon**

*Graduate School of Information, Production and Systems, Waseda University, Hibikino 2-7, Wakamatsu-ku, Kitakyushu, Fukuoka 803-0135, Japan

**The Product Development HQ of Fujitec Co. Ltd., 1-28-10 Sho, Ibaraki, Osaka, Japan

Received:
May 9, 2005
Accepted:
November 9, 2005
Published:
May 20, 2006
Keywords:
elevator group supervisory control system, evolutionary computation, genetic network programming, functional localization, real-coded genetic algorithm
Abstract

Genetic Network Programming (GNP) having a directed graph structure has been proposed as a new method of evolutionary computation. Recently, GNP has been applied to elevator group supervisory control system (EGSCS), a real-world problem, to demonstrate its applicability and effectiveness. Its previous study considers the known and fixed traffic flow, however, it is changed dynamically with time in real elevator systems. Therefore, an EGSCS with dynamic adaptive control considering such changes should be studied for practical applications. In this paper, we have applied GNP with functional localization to an EGSCS to construct such an adaptive system. In our proposal, the switching GNP can switch the functionally localized GNPs (assigning GNPs) based on the special traffic. Simulation confirmed the adaptability and effectiveness of our proposal in daily office-building traffic.

Cite this article as:
Toru Eguchi, Jin Zhou, Shinji Eto, Kotaro Hirasawa,
Jinglu Hu, and Sandor Markon, “Elevator Group Supervisory Control System Using Genetic Network Programming with Functional Localization,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.3, pp. 385-394, 2006.
Data files:
References
  1. [1] K. Hirasawa, M. Okubo, J. Hu, and J. Murata, “Comparison between Genetic Network Programming (GNP) and Genetic Programming (GP),” in Proc. of IEEE Congress on Evolutionary Computation, pp. 1276-1282, 2001.
  2. [2] H. Katagiri, K. Hirasawa, and J. Hu, “Network structure oriented evolutionary model –Genetic Network Programming– and its comparison with Genetic Programming,” in 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, pp. 219-226, 2001.
  3. [3] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “A Study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE Trans. on Systems, Man and Cybernetics, Part B, Vol.35, No.1, 2006.
  4. [4] G. Barney, “Elevator Traffic Handbook,” Spon Press, 2003.
  5. [5] T. Eguchi, K. Hirasawa, J. Hu, and S. Markon, “Elevator Group Supervisory Control Systems using Genetic Network Programming,” in Proc. of IEEE Congress on Evolutionary Computation 2004, pp. 1661-1667, 2004.
  6. [6] T. Eguchi, J. Zhu, K. Hirasawa, J. Hu, and S. Markon, “Basic Study of Elevator Group Supervisory Control Systems using Genetic Network Programming,” Trans. IEE Japan, Vol.125-C, No.7, pp. 1055-1062, 2005 (in Japanese).
  7. [7] C. Kim, K. Seong, H. Lee-Kwang, and J. O. Kim, “Design and Implementation of a Fuzzy Elevator Group Control System,” IEEE Trans. on Systems, Man and Cybernetics, Part A, Vol.28, No.3, pp. 277-287, 1998.
  8. [8] S. Eto, K. Hirasawa, and J. Hu, “Functional Localization of Genetic Network Programming and its Application to a Pursuit Problem,” in Proc. of IEEE Congress on Evolutionary Computation 2004, pp. 683-690, 2004.
  9. [9] S. Markon, H. Kita, and Y. Nishikawa, “Adaptive optimal elevator group control by use of neural networks,” Transactions of the Institute of Systems, Control and Information Engineers, Vol.7, pp. 487-497, 1994.
  10. [10] B. A. Powell, D. J. Sirag, and B. L. Whitehall, “Artificial neural networks in elevator dispatching,” Elevatori, pp. 44-57, 2000.
  11. [11] H. Wan, C. Liu, and H. Liu, “NN Elevator Group-Control Method,” Elevator World, 2, pp. 149-154, 2003.
  12. [12] A. Fujino, T. Tobita, K. Segawa, K. Yoneda, and A. Togawa, “An elevator group control system with floor-attribute control method and system optimization using genetic algorithms,” IEEE Trans. on Ind. Electron., Vol.44, No.4, pp. 546-552, 1997.
  13. [13] S. Takahashi, H. Kita, H. Suzuki, T. Sudo, and S. Markon, “Simulation-based Optimization of a Controller for Multi-Car Elevators Using a Genetic Algorithm for Noisy Fitness Function,” in Proc. of IEEE Congress on Evolutionary Computation 2003, pp. 1582-1587, 2003.
  14. [14] X. Bi, C. Zhu, and Q. Ye, “A GA-Based Approach to the Multi-Objective Optimization Problem in Elevator Group Control System,” Elevator World, 6, pp. 58-63, 2004.
  15. [15] P. Cortes, J. Larraneta, and L. Onieva, “Genetic algorithm for controllers in elevator groups: analysis and simulation during lunchpeak traffic,” Applied Soft Computing 4, pp. 159-174, 2004.
  16. [16] R. Gudwin, F. Gomide, and M. Netto, “A fuzzy elevator group controller with linear context adaptation,” in Proc. of FUZZ-IEEE98, WCCI’98 – IEEE World Congress on Computational Intelligence, pp. 481-486, 1998.
  17. [17] R. Crites, and A. Barto, “Elevator Group Control Using Multiple Reinforcement Learning Agents,” Machine Learning, 33, pp. 235-262, 1998.
  18. [18] Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs,” Second, extended edition, Springer, 1994.
  19. [19] K. Hirasawa, S. Kuzunuki, T. Iwasaka, T. Kaneko, and K. Kawatake, “A Call Allocating Method of Elevator Group Supervisory Control,” Trans. IEE Japan, Vol.99-C, No.2, pp. 27-32, 1979 (in Japanese).
  20. [20] K. Kurosawa, K. Hirasawa, S. Kuzunuki, K. Yoneda, Y. Sakai, and H. Haginaka, “An Intelligent Group Supervisory Control of Elevators,” Trans. IPS Japan, Vol.26, No.2, pp. 278-287, 1985 (in Japanese).
  21. [21] G. Barney, and S. dos Santos, “Elevator Traffic Analysis, Design and Control,” second ed., Peter Peregrinus Ltd., London, 1985.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Aug. 03, 2021