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F. Luo, Y. G. Xu and J. Z. Cao, “Elevator Traffic Flow Prediction with Least Squares Support Vector Machines,” International Conference on Machine Learning and Cybernetics, Vol. 7, 2005, pp. 4266-4270.

has been cited by the following article:

• TITLE: Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

  AUTHORS: Yanwu Gu

  KEYWORDS: Multi-Agent System; Elevator Group Control System; Real-Time Particle Swarm Optimization; Up-Peak Traffic

  JOURNAL NAME: Engineering, Vol.4 No.7, July 3, 2012

  ABSTRACT: In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.