The Euclidean Minimum Sum-of-Squares Clustering(MSSC) is one of the most important models for the clustering problem. Due to its NP-hardness, the problem continues to receive much attention in the scientific literature and several heuristic procedures have been proposed. Recent research has been devoted to the improvement of the classical K-MEANS algorithm, either by suitably selecting its starting configuration or by using it as a local search method within a global optimization algorithm. This paper follows this last approach by proposing a new implementation of a Memetic Differential Evolution (MDE) algorithm specifically designed for the MSSC problem and based on the repeated execution of K-MEANS from selected configurations. In this paper we describe how to adapt MDE to the clustering problem and we show, through a vast set of numerical experiments, that the proposed method has very good quality, measured in terms of the minimization of the objective function, as well as a very good efficiency, measured in the number of calls to the local optimization routine, with respect to state of the art methods.

该欧几里德最小总和法方聚类（MSSC）是聚类问题中最重要的车型之一。由于其NP硬度，该问题在科学文献中继续受到广泛关注，并且已经提出了几种启发式程序。最近的研究致力于改进经典的K-MEANS算法，方法是适当地选择其初始配置，或者将其用作全局优化算法中的局部搜索方法。本文遵循了最后一种方法，提出了一种专为MSSC设计的Memetic差分演化（MDE）算法的新实现。问题，并基于从选定配置中重复执行K-MEANS的过程。在本文中，我们描述了如何使MDE适应聚类问题，并且通过大量的数值实验表明，从目标函数的最小化角度来看，该方法具有很好的质量。相对于最新方法，以对本地优化例程的调用次数来衡量，效率很高。