和声搜索算法在聚类分析中的应用

提出了一种改进的和声搜索算法并应用到聚类分析中.首先,将状态反馈机制引入到和声搜索算法中,通过判断和声记忆库中"最优"和声和"最差"和声之间的差异,来动态调整和声记忆库考虑概率和移动步长,使算法能够快速地收敛到全局最优解.通过更新和声向量中精度变量对应的聚类中心来最小化目标函数值,获得数据样本的最优划分.其次,提出了一种数据样本真实聚类中心数的确定方法,当输入样本数大于真实聚类中心数时,通过计算能够自动地确定数据样本真实聚类中心数目.最后,应用4种性能指标来比较所提算法与蚁群聚类算法和原始和声搜索聚类算法的性能.结果表明,所提算法的性能优于另两种算法.

The Application of Harmony Search Algorithm in Clustering Analysis

An improved harmony search algorithm(IHS) was proposed and used in clustering analysis.The feedback mechanism was introduced first into the harmony search algorithm,and harmony memory considering rate and bandwidth can be dynamically adjusted by calculating the difference between the best harmony and the worst harmony,which makes the IHS converge to the global optimal solution quickly.The proposed algorithm updates the decision variables which represent the cluster centers to minimize the value of objective function and get the best partition of data samples.A method which can automatically determine the true number of clusters of data samples was then proposed,the true number of clusters was calculated when the input number of cluster center is greater than the true number of clusters.Finally,four performance measures were used to compare the proposed algorithm with ANT-based and traditional harmony search(HS) based clustering analysis algorithms,it is shown that the performance of the proposed algorithm is better than the other two algorithms.