基于人工免疫聚类算法的电梯交通流分析

采用人工免疫聚类算法对某大楼一周内的电梯交通流进行了分析．利用人工免疫算法的免疫激励和免疫抑制机制，对电梯交通的5分钟原始客流数据进行压缩，得到了特点鲜明的电梯交通流人工免疫记忆数据集．以最小支撑树为工具，采用最短距离法对记忆数据集进行分类，所得结果清晰地体现了电梯交通流的实际特性．该算法为电梯群的控制与调度提供了有力的理论支持．

Elevator Traffic Flow Analysis Based on Artificial Immune Clustering Algorithm

By employing an artificial immune clustering algorithm, the elevator traffic flow in a certain building during one week was analyzed. The promotion and suppression mechanisms of artificial immune algorithm were exploited to compress the original elevator traffic flow data collected in 5 min, and a distinct elevator traffic flow memory dataset was obtained. The memory dataset was then classified on the basis of two methods: the Minimal Spanning Tree and the Minimal Distance Method. The result of classification clearly reveals the real feature of elevator traffic. Therefore, the approach addressed here may provide a powerful theoretical support for elevator group control.