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Mathematical Problems in Engineering
Volume 2016, Article ID 9791060, 10 pages
Research Article

Particle Swarm and Bacterial Foraging Inspired Hybrid Artificial Bee Colony Algorithm for Numerical Function Optimization

1Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things, Jiangnan University, Wuxi, Jiangsu 214122, China
2Laboratory of Computational Geodynamics, University of Chinese Academy of Sciences, Beijing 100049, China
3Freshwater Fisheries Research Center, Chinese Academy of Fishery Science, Wuxi, Jiangsu 214081, China

Received 4 November 2015; Revised 4 January 2016; Accepted 4 January 2016

Academic Editor: Matjaz Perc

Copyright © 2016 Li Mao 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) algorithm has good performance in discovering the optimal solutions to difficult optimization problems, but it has weak local search ability and easily plunges into local optimum. In this paper, we introduce the chemotactic behavior of Bacterial Foraging Optimization into employed bees and adopt the principle of moving the particles toward the best solutions in the particle swarm optimization to improve the global search ability of onlooker bees and gain a hybrid artificial bee colony (HABC) algorithm. To obtain a global optimal solution efficiently, we make HABC algorithm converge rapidly in the early stages of the search process, and the search range contracts dynamically during the late stages. Our experimental results on 16 benchmark functions of CEC 2014 show that HABC achieves significant improvement at accuracy and convergence rate, compared with the standard ABC, best-so-far ABC, directed ABC, Gaussian ABC, improved ABC, and memetic ABC algorithms.