Title: Artificial bee colony algorithm with multi-strategy adaptation

Authors: Zhaolu Guo; Hongjin Li; Wensheng Zhang

Addresses: School of Science, Jiangxi University of Science and Technology, Ganzhou, 341000, China; Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China ' School of Science, Jiangxi University of Science and Technology, Ganzhou, 341000, China ' Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

Abstract: To improve the convergence performance of artificial bee colony (ABC) algorithm for tackling some complex optimisation issues, a new ABC with multi-strategy adaptation (MSABC) is presented. A multi-strategy adaptation mechanism is implemented to boost the search performance in MSABC. In this mechanism, an evolution rate index is proposed to adaptively select strategies with different characteristics at various evolutionary stages. Meanwhile, for balancing exploration and exploitation, a novel search strategy oriented by the elite individual is applied in this mechanism. On the CEC2014 test set, MSABC is compared to other existing algorithms to assess its performance. As the results demonstrate, MSABC can obtain good convergence performance.

Keywords: artificial bee colony algorithm; multi-strategy adaptation; evolution process; elite orientation.

DOI: 10.1504/IJBIC.2024.137917

International Journal of Bio-Inspired Computation, 2024 Vol.23 No.3, pp.135 - 147

Received: 22 Mar 2023
Accepted: 21 Sep 2023
Published online: 08 Apr 2024 *

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