基于改进蜂群算法的K-means算法

针对标准人工蜂群算法搜索效率低、收敛速度慢等缺点提出一种改进的人工蜂群算法.通过引入算术交叉操作以及利用最优解指导搜索方向,增加算法收敛的速度.在7个基准函数上的测试结果表明了算法的有效性.在此基础上,针对K-means算法的缺点提出基于改进蜂群算法的K-means算法,并加入自动获得最佳聚类数的功能.在人工数据集和UCI真实数据集上的测试验证了所提出算法的性能.

K-means algorithm based on improved artificial bee colony algorithm

In order to overcome the disadvantage of the canonical artificial bee colony algorithm, which has low search efficiency and slow convergence, an improved artificial bee colony algorithm is proposed. This algorithm increases the convergence speed by introducing the arithmetic crossover operation and guiding the search direction by the global best solution. The proposed algorithm is proved to be effective with a test on seven benchmark functions. On the basis of previous work, according to the drawbacks of the K-means algorithm, the K-means algorithm based on the improved artificial bee colony algorithm is proposed, and the function of automatically selecting the best number of clusters is added. A test on the artificial data sets and UCI real data sets verifies the performance of the proposed algorithm.