摘要： 蚁群优化是人工智能领域中群体智能分支之一,已经成功地应用于旅行推销员、作业调度由选择等优化问题上,但用它解决数据挖掘问题还是一个新的研究课题。Parepinelli等人针对单一数据库提出了基于ACO的分类算法[1]。该文提出了基于分布式数据库体系结构的ACO分类算法,采用了与Parepinelli算法不同的启发函数计算方法及信息素改变方法,模拟实验表示该方法是有效的。

关键词: 群体智能, 蚁群优化, 分类, 分布式数据库

Abstract: Ant colony optimization (ACO) is a branch of a newly developed form of artificial intelligence called swarm intelligence. It has been applied successfully to travel sales problem, job schedule , router choice and other combinatorial optimization problems. But it is still a new research topic in data mining. Parepinelli etc. proposed ACO classification algorithm to single database[1]. The paper proposes ACO classification algorithm based on distributed database structure , and uses new heuristic computation and pheromone update methods which are different from Parepinelli’s. The experiment shows that the method is effective.

中图分类号: 

• TP18