Title: Artificial bee colony algorithm with accelerating convergence

Authors: Li Lv; Longzhe Han; Tanghuai Fan; Jia Zhao

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China; Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330099, China

Abstract: To overcome the drawbacks of Artificial Bee Colony (ABC) algorithm, which converges slowly in the process of searching and easily suffers from premature, this paper presents an effective approach, called ABC with accelerating convergence (AC-ABC). In the process of evolution, first, the employed bee's position is regarded as the general centre position, the bees choose a location greedily as the new global optimal position in the original and general centre position; then we put the advantage of global optimal bee into evolution rule; we add the ability of best bee's learning into the standard ABC and reduce the value of convergence factor linearly according to the iteration times, which can improve the convergence of the new algorithm effectively. Experiments are conducted on 12 test functions to verify the performance of AC-ABC; the results demonstrate promising performance of our method AC-ABC on convergence velocity, precision, and stability of solution.

Keywords: artificial bee colony; ABC; general centre location; evolution rule; accelerating convergence; swarm intelligence.

DOI: 10.1504/IJWMC.2016.075222

International Journal of Wireless and Mobile Computing, 2016 Vol.10 No.1, pp.76 - 82

Received: 01 Jun 2015
Accepted: 01 Jul 2015
Published online: 08 Mar 2016 *

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