关键词: LF蚁群聚类算法, 直接分配, 全局记忆, 余弦相似度, 相异原则

Abstract: The traditional LF ant colony clustering algorithm has been studied, such as slow convergence rate, waste of resources caused by empty ant no-load and easy to get into local optimum. In order to improve the convergence rate, the principle of direct allocation is adopted in the initial stage of the algorithm, putting the ants on the data point at random and generating random global memory. The movement of loaded ants is guided by global memory during clustering, moving to the memory center which is using cosine similarity to determine the most similar. Global memory is updated after an iteration is complete. When the ant failed to pick up the data object, ?the principle of dissimilarity is adopted to move ants to the next data point in order to reduce the resource waste caused by the random movement of ants again. The improved algorithm which improves the convergence speed greatly on the basis of ensuring the accuracy of the original algorithm is validated with Iris, Wine, Glass and Robotnavigation in the UCI data set.

Key words: LF ant colony clustering, direct?distribution, global memory, cosine similarity, principle of dissimilarity