Title: Enhancing artificial bee colony algorithm with generalised opposition-based learning

Authors: Xinyu Zhou; Zhijian Wu; Changshou Deng; Hu Peng

Addresses: School of Computer and Information Engineering, Jiangxi Normal University, Nanchang 330022, China ' State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China ' School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China ' State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China

Abstract: As a new global optimisation technique, artificial bee colony (ABC) algorithm becomes popular in recent years for its simplicity and effectiveness. In the basic ABC, however, the solution search equation updates only one dimension to produce a new candidate solution, which may result in that the offspring becomes similar to its parent and cause insufficient search. To overcome this drawback, we proposes an enhanced ABC (EABC) variant by utilising the generalised opposition-based learning (GOBL) strategy. With the help of GOBL, much more promising search regions can be explored, so the probability of converging to the global optimum is highly increased. Experiments are conducted on 13 well-known benchmark functions to verify the proposed approach, and the results show that EABC is very promising in terms of solution accuracy and convergence speed.

Keywords: artificial bee colony; ABC; generalised opposition-based learning; GOBL; global optimisation; swarm intelligence.

DOI: 10.1504/IJCSM.2015.069746

International Journal of Computing Science and Mathematics, 2015 Vol.6 No.3, pp.297 - 309

Received: 06 Sep 2014
Accepted: 27 Oct 2014
Published online: 08 Jun 2015 *

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