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Computational Intelligence and Neuroscience
Volume 2016, Article ID 9820294, 13 pages
Research Article

A New Modified Artificial Bee Colony Algorithm with Exponential Function Adaptive Steps

1Department of Mathematics, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China
2Key Laboratory of Higher Education of Sichuan Province for Enterprise Informationalization and Internet of Things, Zigong, Sichuan 643000, China
3School of Automation and Electronic Information, Sichuan University of Science & Engineering, Zigong, Sichuan 643000, China

Received 26 July 2015; Revised 10 October 2015; Accepted 19 October 2015

Academic Editor: Yufeng Zheng

Copyright © 2016 Wei 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.


As one of the most recent popular swarm intelligence techniques, artificial bee colony algorithm is poor at exploitation and has some defects such as slow search speed, poor population diversity, the stagnation in the working process, and being trapped into the local optimal solution. The purpose of this paper is to develop a new modified artificial bee colony algorithm in view of the initial population structure, subpopulation groups, step updating, and population elimination. Further, depending on opposition-based learning theory and the new modified algorithms, an improved -type grouping method is proposed and the original way of roulette wheel selection is substituted through sensitivity-pheromone way. Then, an adaptive step with exponential functions is designed for replacing the original random step. Finally, based on the new test function versions CEC13, six benchmark functions with the dimensions and are chosen and applied in the experiments for analyzing and comparing the iteration speed and accuracy of the new modified algorithms. The experimental results show that the new modified algorithm has faster and more stable searching and can quickly increase poor population diversity and bring out the global optimal solutions.