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

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