As an efficient data mining technique, data clustering has been widely-used for data analysis and extracting valuable hidden information. Leveraging the simplicity and effectiveness, the evolutionary optimization-driven clustering algorithms have exhibited promising performance and attracted tremendous attention. Up to the present, how to enable these algorithms to escape from local optima and accelerate convergence rates is an ongoing challenge. In this paper, we propose a novel adaptive Differential Evolution (DE) variant to deal with the above challenge when clustering data. In the improved DE algorithm, the four interdependent components, including mutation strategy, crossover strategy, scaling factor value, and crossover rate, are adaptively configured in an integrated manner via ant colony optimization (ACO) during the problem-solving process. To be specific, the relationships of four components in the DE algorithm are modeled as a directed acyclic graph, and a path in the graph exactly corresponds to a configuration for DE. During the optimization process, ant colony optimization is employed to search for a reasonable path for each individual of DE in terms of pheromones on arcs. In this manner, the configuration of the four interdependent components of DE will be generated dynamically, which is then used to guide the successive search behaviors of individuals in DE. Each individual has a path, representing a configuration for each component. After each iteration, individuals that generate promising solutions are allowed to deposit pheromone on the paths, resulting in more pheromones on the arcs appearing in better algorithm configurations (paths) more frequently. Through this manner, the search strategies and parameters of DE are comprehensively adapted by ACO. The proposed algorithm is named ACODE for short. To verify its effectiveness, the proposed ACODE is compared with four representative data clustering algorithms on eight widely-used benchmark datasets. The experimental results demonstrate the advantages of ACODE over half of the datasets.

作为一种有效的数据挖掘技术，数据聚类已广泛用于数据分析和提取有价值的隐藏信息。利用简单性和有效性，进化优化驱动的聚类算法表现出令人鼓舞的性能，并引起了极大的关注。到目前为止，如何使这些算法摆脱局部最优并加快收敛速度​​是一个持续的挑战。在本文中，我们提出了一种新颖的自适应差分进化（DE）变体，以在对数据进行聚类时应对上述挑战。在改进的DE算法中，包括突变策略，交叉策略，比例因子值和交叉率这四个相互依存的组件，在解决问题的过程中，通过蚁群优化（ACO）以集成的方式自适应地配置作业。具体而言，将DE算法中四个组件的关系建模为有向无环图，并且图中的路径正好对应于DE的配置。在优化过程中，采用蚁群优化来为DE的每个个体根据弧上的信息素搜索合理的路径。以这种方式，将动态生成DE的四个相互依赖的组件的配置，然后将其用于指导DE中个人的连续搜索行为。每个人都有一条路径，代表每个组件的配置。每次迭代后，允许产生有希望的解决方案的个人在路径上沉积信息素，导致弧上的更多信息素更频繁地出现在更好的算法配置（路径）中。通过这种方式，ACO对DE的搜索策略和参数进行了全面调整。该算法简称为ACODE。为了验证其有效性，将拟议的ACODE与八个广泛使用的基准数据集上的四种代表性数据聚类算法进行了比较。实验结果证明了ACODE在一半数据集上的优势。将拟议的ACODE与八个广泛使用的基准数据集上的四个代表性数据聚类算法进行了比较。实验结果证明了ACODE在一半数据集上的优势。将拟议的ACODE与八个广泛使用的基准数据集上的四个代表性数据聚类算法进行了比较。实验结果证明了ACODE在一半数据集上的优势。