JACIII Vol.11 No.9 pp. 1149-1158
doi: 10.20965/jaciii.2007.p1149


Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation

Lu Yu*, Jin Zhou*, Shingo Mabu*, Kotaro Hirasawa*, Jinglu Hu*, and Sandor Markon**

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

**Fujitec Co. Ltd. Prod. Dev. HQ, Big Wing, Hikone, Shiga 522-8588, Japan

March 28, 2007
May 30, 2007
November 20, 2007
elevator group supervisory control system, genetic network programming, ant colony optimization, hybrid algorithm
Recently, Artificial Intelligence (AI) technology has been applied to many applications. As an extension of Genetic Algorithm (GA) and Genetic Programming (GP), Genetic Network Programming (GNP) has been proposed, whose gene is constructed by directed graphs. GNP can perform a global searching, but its evolving speed is not so high and its optimal solution is hard to obtain in some cases because of the lack of the exploitation ability of it. To alleviate this difficulty, we developed a hybrid algorithm that combines Genetic Network Programming (GNP) with Ant Colony Optimization (ACO) with Evaporation. Our goal is to introduce more exploitation mechanism into GNP. In this paper, we applied the proposed hybrid algorithm to a complicated real world problem, that is, Elevator Group Supervisory Control System (EGSCS). The simulation results showed the effectiveness of the proposed algorithm.
Cite this article as:
L. Yu, J. Zhou, S. Mabu, K. Hirasawa, J. Hu, and S. Markon, “Double-Deck Elevator Group Supervisory Control System Using Genetic Network Programming with Ant Colony Optimization with Evaporation,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.9, pp. 1149-1158, 2007.
Data files:
  1. [1] T. Eguchi, K. Hirasawa, J. Hu, and N. Ota, “Study of Evolutionary Multiagent Models Based on Symbiosis,” IEEE Transactions on Systems, Man, and Cybernetics, Part-B, Vol.35, No.1, pp. 179-193, 2006.
  2. [2] S.Mabu, K. Hirasawa, and J. Hu, “A Graph-Based Evolutionary Algorithm: Genetic Network Programming (GNP) and Its Extension Using Reinforcement Learning,” Evolutionary Computation, MIT Press (to appear).
  3. [3] M. Dorigo, V. Maniezzo, and A. Colorni, “Ant System: Optimization by a Colony of Cooperating Agents,” IEEE Transactions on Systems, Man, and Cybernetics, Part-B, Vol.26, No.1, pp. 29-41, Feb., 2006.
  4. [4] M. Dorigo and L. M. Gambardella, “Ant colony system: a cooperative learning approach to the traveling salesman problem,” IEEE Transactions on Evolutionary Computation, Vol.1, No.1, pp. 53-66, Apr., 1997.
  5. [5] T. Stutzle and H. Hoos, “MAX-MIN ant system and local search for the traveling salesman provlem,” Proc. IEEE Int. Conf. Evolutionary Computation, pp. 309-314, 1997.
  6. [6] H. Duan, “Ant Colony Algorithms: theory and applications,” Science Press, 2005.
  7. [7] G. Barney and S. dos Santos, “Elevator Traffic Analysis, Design and Control, Second Ed.,” Peter Peregrinus Ltd., 1985.
  8. [8] R. D. Peters, “The theory and practice of general analysis of lift calculations,” Elevcon Proc., pp. 197-206, 1992.
  9. [9] J. Koehler and D. Ottiger, “An AI-based approach to destination control in elevators,” AI Magazine, 23(3), pp. 59-79, 2002.
  10. [10] S. Tanaka, Y. Innami, and M. Araki, “A study on objective functions for dynamic operation optimization of a single-car elevator system with destination hall call registration,” Proc. IEEE Int. Conf. on Systems, Man and Cyvernetics, 2004.
  11. [11] M.-L. Siikonen, “Double-Deck Elevators: Savings in time and space,” Elevator World, June, 1998.

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Last updated on Jun. 03, 2024