Title: Accelerating artificial bee colony algorithm using elite information

Authors: Xinyu Zhou; Yanlin Wu; Shuixiu Wu; Maosheng Zhong; Mingwen Wang

Addresses: School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China ' School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China ' School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China ' School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China ' School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, 330022, China

Abstract: In nature, the foraging behaviour of bee colony is always guided by some elite honeybees with the aim of maximising the overall nectar amount. Being inspired by this phenomenon, we propose an improved artificial bee colony (ABC) variant by using elite information. In our approach, as the main way of generating new offspring, two novel solution search equations are developed based on utilising elite information, which has the advantages of accelerating convergence rate. Moreover, to preserve the search experience of the scout bee phase, a new reinitialisation method is proposed based on using elite information. Extensive experiments are conducted on the CEC 2013 and CEC 2015 test suites, and other four relevant ABC variants are included in the comparison. The results show that our approach has better performance in terms of convergence speed and result accuracy.

Keywords: artificial bee colony; ABC; solution search equation; elite information; exploration and exploitation.

DOI: 10.1504/IJICA.2022.128440

International Journal of Innovative Computing and Applications, 2022 Vol.13 No.5/6, pp.325 - 335

Received: 28 Aug 2020
Accepted: 18 Jan 2021
Published online: 23 Jan 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article