基于人工鱼群混合聚类的大型水泵分类研究

Research of Large-Scale Water Pump Classification Based on Artificial Fish Swarm Mixed Clustering

摘要: 人工鱼群是一种随机搜索优化算法，具有较快的收敛速度，对问题的机理模型与描述无严格要求，具有广泛的应用范围。本文在该算法的基础上，结合传统的K-means聚类方法，提出了一种新的人工鱼群混合聚类算法(AFSMC)，并给出相对熵混合聚类优化方法。文中将几种算法应用于大型水泵数据的分类，算例分析表明，基于相对熵的AFSMC算法在准确率与时间效益上都具有较明显的优越性。

Abstract: Artificial Fish Swarm (AFS) is a stochastic search optimization algorithm with a fast convergence speed, and it is widely used in different areas due to its tolerance for descriptions and mechanism model of questions. In this paper, we propose a new clustering algorithm called Artificial Fish Swarm Mixed Clustering (AFSMC) by performing a combination of AFS and similarity based K-means. And we further propose a relative entropy based optimization method for AFSMC. Finally, we evaluate our proposal by conducting classification on the real data of large scale water pump, which is a new thought for analyzing the potential correlations among the different pumps. The experimental results show that our proposal outperforms the existing clustering approaches.