Evolutionary algorithm (EA) incorporating with k-means local search operator represents an important approach for cluster analysis. In the existing EA approach, however, the k-means operators are usually directly employed on the individuals and generally applied with fixed intensity as well as frequency during evolution, which could significantly limit their performance. In this paper, we first introduce a hybrid EA based clustering framework such that the frequency and intensity of k-means operator could be arbitrarily configured during evolution. Then, an adaptive strategy is devised to dynamically set its frequency and intensity according to the feedback of evolution. Further, we develop an opposite search strategy to implement the proposed adaptive k-means operation, thus appropriately exploring the search space. By incorporating the above two strategies, a memetic algorithm with adaptive and opposite k-means operation is finally proposed for data clustering. The performance of the proposed method has been evaluated on a series of data sets and compared with relevant algorithms. Experimental results indicate that our proposed algorithm is generally able to deliver superior performance and outperform related methods.

结合k均值局部搜索算子的进化算法（EA）代表了一种重要的聚类分析方法。但是，在现有的EA方法中，k均值算子通常直接应用于个体，并且在进化过程中通常以固定的强度和频率应用，这可能会大大限制其性能。在本文中，我们首先介绍一个基于混合EA的聚类框架，以便在进化过程中可以任意配置k均值算子的频率和强度。然后，设计了一种自适应策略，根据进化反馈动态设置其频率和强度。此外，我们开发了一种相反的搜索策略来实现所提出的自适应k均值运算，从而适当地探索搜索空间。通过结合以上两种策略，最终提出了一种具有自适应k均值和相反k均值运算的模因算法。在一系列数据集上评估了该方法的性能，并与相关算法进行了比较。实验结果表明，我们提出的算法通常能够提供出色的性能，并且性能优于相关方法。