Biologically inspired computing techniques are very effective and useful in many areas of research including data clustering. Ant clustering algorithm is a nature-inspired clustering technique which is extensively studied for over two decades. In this study, we extend the ant clustering algorithm (ACA) to a hybrid ant clustering algorithm (hACA). Specifically, we include a genetic algorithm in standard ACA to extend the hybrid algorithm for better performance. We also introduced novel pick up and drop off rules to speed up the clustering performance. We study the performance of the hACA algorithm and compare with standard ACA as a benchmark.

中文翻译：

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基于混合蚁群的数据聚类

受生物启发的计算技术在包括数据聚类在内的许多研究领域都非常有效和有用。蚂蚁聚类算法是一种受自然启发的聚类技术，已被广泛研究了二十多年。在这项研究中，我们将蚂蚁聚类算法（ACA）扩展为混合蚂蚁聚类算法（hACA）。具体来说，我们在标准 ACA 中包含了一种遗传算法，以扩展混合算法以获得更好的性能。我们还引入了新颖的接送规则以加快聚类性能。我们研究了 hACA 算法的性能，并与作为基准的标准 ACA 进行了比较。