Abstract
Artificial bee colony (ABC) algorithm has been widely used to solve the optimization problems. In the existing ABC algorithms, choosing which employed bee giving up its food source only based on its current trial number. It may cause some promising areas are exploited insufficiently and some non-significant areas are searched excessively, which leads to a waste of much more searching resources. To cope with this problem, an improved exhausted food source identification mechanism based on space partitioning is designed, which considers the food source states both in the objective space and searching space simultaneously. Then, the proposed mechanism is applied to the basic ABC algorithm and a recently improved ABC algorithm. The experimental results have demonstrated that the ABC algorithms with the designed exhausted food source identification mechanism perform better than the original ABC algorithms in almost all the functions on the CEC2015 test suit.





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References
Bai, W., Eke, I., & Lee, K. Y. (2017). An improved artificial bee colony optimization algorithm based on orthogonal learning for optimal power flow problem. Control Engineering Practice, 61, 163–172.
Cui, L., Zhang, K., Li, G., Fu, X., Wen, Z., Lu, N., et al. (2018). Modified gbest-guided artificial bee colony algorithm with new probability model. Soft Computing, 22(7), 2217–2243.
Hearn, D., Baker, M. P., et al. (2004). Computer graphics with OpenGL. Upper Saddle River, NJ: Pearson Prentice Hall.
Huo, L., & Jiang, D. (2019). Stackelberg game-based energy-efficient resource allocation for 5G cellular networks. Telecommunication Systems, 72(3), 377–388.
Huo, L., Jiang, D., & Lv, Z. (2018). Soft frequency reuse-based optimization algorithm for energy efficiency of multi-cell networks. Computers & Electrical Engineering, 66, 316–331.
Huo, L., Jiang, D., Zhu, X., Wang, Y., Lv, Z., & Singh, S. (2019). A SDN‐based fine‐grained measurement and modeling approach to vehicular communication network traffic. International Journal of Communication Systems, (e4092), 1–19.
Jiang, D., Xu, Z., Li, W., & Chen, Z. (2017). Topology control-based collaborative multicast routing algorithm with minimum energy consumption. International Journal of Communication Systems, 30(1), e2905.
Jiang, D., Huo, L., & Li, Y. (2018). Fine-granularity inference and estimations to network traffic for SDN. PloS One, 13(5), e0194302.
Jiang, D., Huo, L., Lv, Z., Song, H., & Qin, W. (2018). A joint multi-criteria utility-based network selection approach for vehicle-to-infrastructure networking. IEEE Transactions on Intelligent Transportation Systems, 19(10), 3305–3319.
Jiang, D., Huo, L., & Song, H. (2018). Rethinking behaviors and activities of base stations in mobile cellular networks based on big data analysis. IEEE Transactions on Network Science and Engineering, 1–12.
Jiang, D., Wang, W., Shi, L., & Song, H. (2018). A compressive sensing-based approach to end-to-end network traffic reconstruction. IEEE Transactions on Network Science and Engineering, 1–14.
Jiang, D., Wang, Y., Lv, Z., Qi, S., & Singh, S. (2019). Big data analysis-based network behavior insight of cellular networks for industry 4.0 applications. IEEE Transactions on Industrial Informatics, 1–9.
Karaboga, D., & Basturk, B. (2007). A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.
Karaboga, D., & Gorkemli, B. (2014). A quick artificial bee colony (qabc) algorithm and its performance on optimization problems. Applied Soft Computing, 23, 227–238.
Karaboga, D., Gorkemli, B., Ozturk, C., & Karaboga, N. (2014). A comprehensive survey: Artificial bee colony (abc) algorithm and applications. Artificial Intelligence Review, 42(1), 21–57.
Kishor, A., Chandra, M., & Singh, P. K. (2017). An astute artificial bee colony algorithm. In: Proceedings of Sixth International Conference on Soft Computing for Problem Solving (pp. 153–162). Springer.
Tang, W., Zhang, K., & Jiang, D. (2018). Physarum-inspired routing protocol for energy harvesting wireless sensor networks. Telecommunication Systems, 67(4), 745–762.
Wang, F., Jiang, D., & Qi, S. (2019). An adaptive routing algorithm for integrated information networks. China Communications, 16(7), 195–206.
Wang, F., Jiang, D., Wen, H., & Song, H. (2019). Adaboost-based security level classification of mobile intelligent terminals. The Journal of Supercomputing, 75(11), 7460–7478.
Xiang, W. L., Meng, X. L., Li, Y. Z., He, R. C., & An, M. Q. (2018). An improved artificial bee colony algorithm based on the gravity model. Information Sciences, 429, 49–71.
Xu, J., Wei, L., Zhang, Y., Wang, A., Zhou, F., & Cz, Gao. (2018). Dynamic fully homomorphic encryption-based merkle tree for lightweight streaming authenticated data structures. Journal of Network and Computer Applications, 107, 113–124.
Yu, W. J., Zhan, Z. H., & Zhang, J. (2016). Artificial bee colony algorithm with an adaptive greedy position update strategy. Soft Computing, 22(17), 1–15.
Zhang, S., Lee, C. K., Chan, H. K., Choy, K. L., & Wu, Z. (2015). Swarm intelligence applied in green logistics: A literature review. Engineering Applications of Artificial Intelligence, 37, 154–169.
Zhong, F., Li, H., & Zhong, S. (2017). An improved artificial bee colony algorithm with modified-neighborhood-based update operator and independent-inheriting-search strategy for global optimization. Engineering Applications of Artificial Intelligence, 58, 134–156.
Acknowledgements
This work was funded by the National Natural Science Foundation Program of China (61572116 and 61572117), and the National Natural Science Foundation Program of Liaoning Province (20170540792). Thanks for the reviewers.
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Ning, J., Zhao, H. & Liu, C. An improved exhausted-food-sources-identification mechanism for the artificial bee colony algorithm. Wireless Netw 27, 3561–3572 (2021). https://doi.org/10.1007/s11276-019-02227-9
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DOI: https://doi.org/10.1007/s11276-019-02227-9