Title: Chaotic artificial bee colony with elite opposition-based learning

Authors: Zhaolu Guo; Jinxiao Shi; Xiaofeng Xiong; Xiaoyun Xia; Xiaosheng Liu

Addresses: Institute of Medical Informatics and Engineering, School of Science, JiangXi University of Science and Technology, Ganzhou, 341000, China ' School of Science, JiangXi University of Science and Technology, Ganzhou, 341000, China ' School of Science, JiangXi University of Science and Technology, Ganzhou, 341000, China ' School of Information Engineering, JiangXi University of Science and Technology, Ganzhou, 341000, China ' School of Architectural and Surveying and Mapping Engineering, JiangXi University of Science and Technology, Ganzhou, 341000, China

Abstract: Artificial bee colony (ABC) algorithm is a promising evolutionary algorithm inspired by the foraging behaviour of honey bee swarm, which has obtained satisfactory solutions in diverse applications. However, the basic ABC often demonstrates insufficient exploitation capability in some cases. To address this concerning issue, a chaotic artificial bee colony with elite opposition-based learning strategy (CEOABC) is proposed in this paper. During the search process, CEOABC employs the chaotic local search to promote the exploitation ability. Moreover, the elite opposition-based learning strategy is utilised to exploit the potential information of the exhausted solution. Experimental results compared with several ABC variants show that CEOABC is a competitive approach for global optimisation.

Keywords: artificial bee colony; ABC; chaotic local search; opposition-based learning; OBL; elite strategy.

DOI: 10.1504/IJCSE.2019.099076

International Journal of Computational Science and Engineering, 2019 Vol.18 No.4, pp.383 - 390

Received: 08 Apr 2016
Accepted: 04 Oct 2016

Published online: 08 Apr 2019 *

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