Elevator group supervisory control system using genetic network programming - Ranking processing and node function optimization

Toru Eguchi, Kotaro Hirasawa, Jinglu Hu, Sandor Markon

Research output: Contribution to conferencePaper

Abstract

Genetic Network Programming (GNP) has been proposed as a new method of evolutionary computations. Recently, GNP is applied to Elevator Group Supervisory Control System (EGSCS), that is, a benchmark of real world applications and its effectiveness is clarified. The EGSCS using GNP in the previous studies can control the elevator system using the conventional node functions. However, they do not have enough flexibility and generality for some uncertain factors due to the various different conditions in elevator systems. In this paper, several new frameworks of GNP for EGSCS are proposed in order to overcome the above problem considering the ranking calculation of elevators and node function optimization based on Real-coded GA. In the simulations, it is clarified that the proposed method can obtain better performances than the conventional methods.

Original languageEnglish
Pages1-6
Number of pages6
Publication statusPublished - 2005 Dec 1
EventSICE Annual Conference 2005 - Okayama, Japan
Duration: 2005 Aug 82005 Aug 10

Conference

ConferenceSICE Annual Conference 2005
CountryJapan
CityOkayama
Period05/8/805/8/10

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Keywords

  • Elevator Group Supervisory Control System
  • Evolutionary Computation
  • Genetic Network Programming
  • Real-coded GA

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Eguchi, T., Hirasawa, K., Hu, J., & Markon, S. (2005). Elevator group supervisory control system using genetic network programming - Ranking processing and node function optimization. 1-6. Paper presented at SICE Annual Conference 2005, Okayama, Japan.