Elevator group supervisory control system using genetic network programming with functional localization

Toru Eguchi, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

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

3 Citations (Scopus)

Abstract

Genetic Network Programming (GNP) whose gene consists of directed graphs has been proposed as a new method of evolutionary computations, and it is recently applied to the Elevator Group Supervisory Control System (EGSCS), a real world problem, to confirm its effectiveness. In the previous study, although the flow of traffic in the elevator system is known and fixed, it is changed dynamically with time in real elevator systems. Therefore, the EGSCS with an adaptive control should be studied considering such changes for practical applications. In this paper, the GNP with functional localization is applied to the EGSCS to construct such an adaptive system. In the proposed method, the switching GNP can switch the functionally localized GNPs (assigning GNPs) fitted to several kinds of traffic by detecting the change of the flow of traffic. From the simulations, the adaptability and effectiveness of the proposed method are clarified using the traffic data of a day in an office building.

Original languageEnglish
Title of host publication2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Pages328-335
Number of pages8
Publication statusPublished - 2005 Oct 31
Event2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 - Edinburgh, Scotland, United Kingdom
Duration: 2005 Sep 22005 Sep 5

Publication series

Name2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings
Volume1

Conference

Conference2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005
CountryUnited Kingdom
CityEdinburgh, Scotland
Period05/9/205/9/5

ASJC Scopus subject areas

  • Engineering(all)

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    Eguchi, T., Hirasawa, K., Hu, J., & Markon, S. (2005). Elevator group supervisory control system using genetic network programming with functional localization. In 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings (pp. 328-335). (2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005. Proceedings; Vol. 1).