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Mathematical Problems in Engineering
Volume 2014, Article ID 832949, 8 pages
http://dx.doi.org/10.1155/2014/832949
Research Article

Hybrid Artificial Bee Colony Algorithm and Particle Swarm Search for Global Optimization

College of Mathematics and Information, Henan Normal University, Xinxiang 453007, China

Received 17 July 2014; Accepted 1 October 2014; Published 28 October 2014

Academic Editor: Guangming Xie

Copyright © 2014 Wang Chun-Feng 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.

Abstract

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.