摘要
提出一种蚁群优化聚类算法,用于将N个对象优化分成K个不同的划分;该算法采用全局信息素更新策略和启发式信息构造聚类解,通过提高信息素在求解过程中的利用率加快了聚类速度,通过使用启发式信息提高了算法的搜索效率,使用均匀交叉算子改善了聚类解的质量;在几个模拟的数据集和UCI机器学习数据集上测试该算法的性能,并与其它几个启发式算法进行比较;计算结果表明该算法具有更好的解的质量,更少的函数估计次数和更少的运行时间。
This paper presents an ant colony clustering algorithm for optimally clustering N objects into K clusters. The algorithm employs the global pheromone updating and the heuristic information to construct clustering solutions. The rate of clustering is accelerated by increasing the utilization of the pheromone. The heuristic information is applied to improve the efficiency of the algorithm. Uniform crossover operator is used to further improve solutions discovered by ants. This algorithm has been implemented and tested on several simulated datasets and UCI machine learning datasets. The performance of this algorithm is compared with other popular heuristic methods. Our computational simulations reveal very encouraging results in terms of the quality of solution found, the average number of function evaluations and the processing time required.
出处
《计算机测量与控制》
CSCD
2007年第11期1590-1592,1596,共4页
Computer Measurement &Control
基金
国家自然科学基金资助项目(50138010)
关键词
蚁群算法
聚类
优化
均匀交叉
ant colony algorithm
clustering
optimization
uniform crossover
