Elevator Group-Control Policy Based on Neural Network Optimized by Genetic Algorithm

• Published in: Transactions of Tianjin University Vol. 15; no. 4; pp. 245 - 248
• Main Author: 沈虹 万健如 张志超 刘英培 李光叶
• Format: Journal Article
• Language: English
• Published: Heidelberg Tianjin University 01.08.2009; School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China%Baoding Power Supply Company, Baoding 071000, China
• Subjects: Engineering; Humanities and Social Sciences; Mechanical Engineering; multidisciplinary; Science; 候车时间; 控制目标; 混合算法; 电梯系统; 电梯群控; 神经网络优化; 评价函数; 遗传算法; genetic algorithm; hybrid algorithm; neural network; elevator group control
• ISSN: 1006-4982; 1995-8196
• DOI: 10.1007/s12209-009-0043-0

Aiming at the diversity and nonlinearity of the elevator system control target, an effective group method based on a hybrid algorithm of genetic algorithm and neural network is presented in this paper. The genetic algorithm is used to search the weight of the neural network. At the same time, the multi-objective-based evaluation function is adopted, in which there are three main indicators including the passenger waiting time, car passengers number and the number of stops. Different weights are given to meet the actual needs. The optimal values of the evaluation function are obtained, and the optimal dispatch control of the elevator group control system based on neural network is realized. By analyzing the running of the elevator group control system, all the processes and steps are presented. The validity of the hybrid algorithm is verified by the dynamic imitation performance.