A study of applying genetic network programming with reinforcement learning to elevator group supervisory control system

Jin Zhou*, Toru Eguchi, 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

5 被引用数 (Scopus)

抄録

Elevator Group Supervisory Control System (EGSCS) is a very large scale stochastic dynamic optimization problem. Due to its vast state space, significant uncertainty, and numerous resource constraints such as finite car capacities and registered hall/car calls, it is hard to manage EGSCS using conventional control methods. Recently, many solutions for EGSCS using Artificial Intelligence (AI) technologies have been reported. Genetic Network Programming (GNP), which is proposed as a new evolutionary computation method several years ago, is also proved to be efficient when applied to EGSCS problem. In this paper, we propose an extended algorithm for EGSCS by introducing Reinforcement Learning (RL) into GNP framework, and expect to make an improvement of the EGSCS' performances since the efficiency of GNP with RL has been clarified in some other studies like tile-world problem. Simulation tests using traffic flows in a typical office building have been made, and the results show an actual improvement of the EGSCS' performances comparing to the algorithms using original GNP and conventional control methods.

本文言語English
ホスト出版物のタイトル2006 IEEE Congress on Evolutionary Computation, CEC 2006
ページ3035-3041
ページ数7
出版ステータスPublished - 2006 12月 1
イベント2006 IEEE Congress on Evolutionary Computation, CEC 2006 - Vancouver, BC, Canada
継続期間: 2006 7月 162006 7月 21

出版物シリーズ

名前2006 IEEE Congress on Evolutionary Computation, CEC 2006

Conference

Conference2006 IEEE Congress on Evolutionary Computation, CEC 2006
国/地域Canada
CityVancouver, BC
Period06/7/1606/7/21

ASJC Scopus subject areas

  • 人工知能
  • ソフトウェア
  • 理論的コンピュータサイエンス

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