A ranking paired based artificial bee colony algorithm for data clustering
by Haiping Xu; Zhengshan Dong; Meiqin Xu; Geng Lin
International Journal of Computing Science and Mathematics (IJCSM), Vol. 16, No. 4, 2022

Abstract: Data clustering aims to partition a dataset into k subsets according to a prespecified similarity measure. It is NP-hard, and has lots of real applications. This paper presents a ranking paired based artificial bee colony algorithm (RPABC) to solve data clustering. First, a chaotic map is employed to generate initial food sources. Second, in order to speed up the search, RPABC uses a ranking paired learning strategy to produce new positions. Finally, the best food source is utilised to guide the search in the onlooker bees' phase. Several datasets from the literature are used to test the RPABC. The computational results show that the proposed method is able to provide high quality clusters, and is more stable than the compared algorithms.

Online publication date: Wed, 01-Feb-2023

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