Abstract: The conventional K-harmonic means is tend to be trapped by local optima. To resolve this problem, a novel K-harmonic means clustering algorithm using enhanced differential evolution technique is proposed. This algorithm improves the global search ability by applying variable-scale Logistic chaotic searching and exponentially increasing crossover probability operator. Numerical experiments show that this algorithm overcomes the disadvantages of the K-harmonic means, and improves the global search ability while achieving faster convergence rate.

Key words: K-harmonic means, differential evolution, Logistic chaotic searching, exponentially increasing crossover probability

摘要： 针对K-调和均值聚类算法易陷入局部最优的缺点，提出了一种基于改进差分进化的K-调和均值聚类算法。该算法通过引入基于Logistic变尺度混沌搜索和指数递增交叉概率算子的差分进化算法来增强全局寻优能力。实验结果表明，该算法能够较好地克服K-调和均值算法的缺点，在保证收敛速度的同时增强了算法的全局搜索能力。

关键词: K-调和均值, 差分进化, Logistic混沌搜索, 指数递增交叉概率