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

Paper:

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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

Received:
March 28, 2007
Accepted:
May 30, 2007
Published:
November 20, 2007
Creative Commons License
Keywords:
elevator group supervisory control system, genetic network programming, ant colony optimization, hybrid algorithm
Abstract
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:
References
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