Ant Colony Algorithm and Fuzzy Neural Network-based Intelligent Dispatching Algorithm of An Elevator Group Control System

• Published in: 2007 IEEE International Conference on Control and Automation pp. 2306 - 2310
• Main Authors: Jianchang Liu, Yiyang Liu
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2007
• Subjects: ant colony algorithm; Communication system traffic control; Control systems; Dispatching; elevator group control; Elevators; Fuzzy control; fuzzy neural network; Fuzzy neural networks; Fuzzy systems; intelligent dispatching; Intelligent networks; Intelligent systems; Neural networks
• ISBN: 9781424408177; 1424408172
• ISSN: 1948-3449
• DOI: 10.1109/ICCA.2007.4376773

To improve the performance of elevator group control systems (EGCS), an intelligent dispatching method based on ant colony algorithm and fuzzy neural network is presented. An elevator group control system based on fuzzy neural network adapts to various traffic flow modes. Using ant colony algorithm to optimize the weights of fuzzy neural network before training with BP algorithm can solve the problem that convergence of weights is easy to be trapped in local optimal values when trained just with BP algorithm. This intelligent dispatching algorithm makes the weights of fuzzy neural network more precise and reasonable. These weights greatly affect the performance of an EGCS. The results of simulation show that ant colony algorithm and fuzzy neural network greatly improves the performance of an EGCS. Its average waiting time is obviously shorter than that of the EGCS that is only based on fuzzy neural network.