Browse > Article
http://dx.doi.org/10.3745/JIPS.02.0111

An Improved Artificial Bee Colony Algorithm Based on Special Division and Intellective Search  

Huang, He (Dept. of Information Technology, Wenzhou Vocational and Technical College)
Zhu, Min (College of Information Science and Technology, Zhejiang Shuren University)
Wang, Jin (School of Computer & Communication Engineering, Changsha University of Science & Technology)
Publication Information
Journal of Information Processing Systems / v.15, no.2, 2019 , pp. 433-439 More about this Journal
Abstract
Artificial bee colony algorithm is a strong global search algorithm which exhibits excellent exploration ability. The conventional ABC algorithm adopts employed bees, onlooker bees and scouts to cooperate with each other. However, its one dimension and greedy search strategy causes slow convergence speed. To enhance its performance, in this paper, we abandon the greedy selection method and propose an artificial bee colony algorithm with special division and intellective search (ABCIS). For the purpose of higher food source research efficiency, different search strategies are adopted with different employed bees and onlooker bees. Experimental results on a series of benchmarks algorithms demonstrate its effectiveness.
Keywords
Artificial Bee Colony; Global Search; Intellective Search; Special Division;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 H. Chen, L. Ma, M. He, X. Wang, X. Liang, L. Sun, and M. Huang, "Artificial bee colony optimizer based on bee life-cycle for stationary and dynamic optimization," IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 2, pp. 327-346, 2017.   DOI
2 A. Bose and K. Mali, "Fuzzy-based artificial bee colony optimization for gray image segmentation," Signal, Image and Video Processing, vol. 10, no. 6, pp. 1089-1096, 2016.   DOI
3 J. Vanus, J. Machac, R. Martinek, P. Bilik, J. Zidek, J. Nedoma, and M. Fajkus, "The design of an indirect method for the human presence monitoring in the intelligent building," Human-centric Computing and Information Sciences, vol. 8, article no. 28, 2018.
4 M. S. Kiran, H. Hakli, M. Gunduz, and H. Uguz, "Artificial bee colony algorithm with variable search strategy for continuous optimization," Information Sciences, vol. 300, pp. 140-157, 2015.   DOI
5 X. Zhang, X. Zhang, and L. Wang, "Antenna design by an adaptive variable differential artificial bee colony algorithm," IEEE Transactions on Magnetics, vol. 54, no. 3, pp. 1-4, 2018.   DOI
6 Z. Ye, M. Zhu, and J. Wang, "On modification and application of the artificial bee colony algorithm," Journal of Information Processing Systems, vol. 14, no. 2, pp. 448-454, 2018.   DOI
7 D. Karaboga and B. Gorkemli, "A quick artificial bee colony (qABC) algorithm and its performance on optimization problems," Applied Soft Computing, vol. 23, pp. 227-238, 2014.   DOI
8 X. Li and G. Yang, "Artificial bee colony algorithm with memory," Applied Soft Computing, vol. 41, pp. 362-372, 2016.   DOI
9 W. Wu, B. Wang, Z. Deng, and H. Zhang, "Secure beamforming for full-duplex wireless powered communication systems with self-energy recycling," IEEE Wireless Communications Letters, vol. 6, no. 2, pp. 146-149, 2017.   DOI
10 S. Wang, J. Xie, Y. Zheng, J. Wang, and T. Jiang, "A method of coupling expected patch log likelihood and guided filtering for image de-noising," Journal of Information Processing Systems, vol. 14, no. 2, pp. 552-562, 2018.   DOI