Elevator group control system using genetic network programming with ACO considering transitions

Lu Yu, Jin Zhou, Shingo Mabu, Kotaro Hirasawa, Takayuki Furuzuki, Sandor Markon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

Genetic Programming Network (GNP), a graph-based evolutionary method, has been proposed several years ago as an extension of Genetic Algorithm (GA) and Genetic Programming (GP). The behavior of GNP is characterized by a balance between exploitation and exploration. To improve the evolving speed and efficiency of GNP, we developed a hybrid algorithm that combines GNP with Ant Colony Optimization (ACO). Pheromone information in the algorithm is updated not only by the fitness but also the frequency of the transitions as dynamic updating. We applied the hybrid algorithm to Elevator Group Supervisory Control Systems (EGSCS), a complex real-world problem. Finally, the simulations verified the efficacy of our proposed method.

Original languageEnglish
Title of host publicationProceedings of the SICE Annual Conference
Pages1330-1336
Number of pages7
DOIs
Publication statusPublished - 2007
EventSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu
Duration: 2007 Sep 172007 Sep 20

Other

OtherSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
CityTakamatsu
Period07/9/1707/9/20

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Keywords

  • Ant colony optimization
  • Elevator group supervisory control system
  • Genetic network programming
  • Hybrid algorithm

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yu, L., Zhou, J., Mabu, S., Hirasawa, K., Furuzuki, T., & Markon, S. (2007). Elevator group control system using genetic network programming with ACO considering transitions. In Proceedings of the SICE Annual Conference (pp. 1330-1336). [4421189] https://doi.org/10.1109/SICE.2007.4421189