Title: A ranking paired based artificial bee colony algorithm for data clustering

Authors: Haiping Xu; Zhengshan Dong; Meiqin Xu; Geng Lin

Addresses: College of Mathematics and Data Science, Minjiang University, Fuzhou 350108, China ' College of Mathematics and Data Science, Minjiang University, Fuzhou 350108, China ' College of Mathematics and Data Science, Minjiang University, Fuzhou 350108, China ' College of Mathematics and Data Science, Minjiang University, Fuzhou 350108, China

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.

Keywords: data clustering; artificial bee colony; ranking; ranking paired learning strategy; chaotic map.

DOI: 10.1504/IJCSM.2022.128661

International Journal of Computing Science and Mathematics, 2022 Vol.16 No.4, pp.389 - 398

Received: 17 Oct 2020
Accepted: 11 Feb 2021
Published online: 01 Feb 2023 *

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