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Journal of Control Science and Engineering
Volume 2017, Article ID 4851493, 13 pages
Research Article

Self-Adaptive Artificial Bee Colony for Function Optimization

1School of Energy and Power Engineering, Changsha University of Science & Engineering, Changsha 410114, China
2Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle, Xiangyang 441053, China
3Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance & Economics, Guiyang 550004, China
4Department of Chemical Engineering, University of Waterloo, ON, Canada N2L 3G1

Correspondence should be addressed to Wen Long; nc.ude.efug.liam@722wl

Received 16 February 2017; Revised 19 April 2017; Accepted 28 May 2017; Published 14 August 2017

Academic Editor: Shen Yin

Copyright © 2017 Mingzhu Tang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Artificial bee colony (ABC) is a novel population-based optimization method, having the advantage of less control parameters, being easy to implement, and having strong global optimization ability. However, ABC algorithm has some shortcomings concerning its position-updated equation, which is skilled in global search and bad at local search. In order to coordinate the ability of global and local search, we first propose a self-adaptive ABC algorithm (denoted as SABC) in which an improved position-updated equation is used to guide the search of new candidate individuals. In addition, good-point-set approach is introduced to produce the initial population and scout bees. The proposed SABC is tested on 12 well-known problems. The simulation results demonstrate that the proposed SABC algorithm has better search ability with other several ABC variants.