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

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

*この研究の対応する著者

研究成果: Conference contribution

13 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルSICE Annual Conference, SICE 2007
ページ1330-1336
ページ数7
DOI
出版ステータスPublished - 2007 12 1
イベントSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007 - Takamatsu, Japan
継続期間: 2007 9 172007 9 20

出版物シリーズ

名前Proceedings of the SICE Annual Conference

Conference

ConferenceSICE(Society of Instrument and Control Engineers)Annual Conference, SICE 2007
国/地域Japan
CityTakamatsu
Period07/9/1707/9/20

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • 電子工学および電気工学

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