Abstract
Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure.
This work was supported in part by the National High-Technology Research and Development Program (863 Program) of China No.2013AA01A212, in part by the National Natural Science Fundation of China (NSFC) with No. 61402545, the NSFC Key Program with No. 61332002, and the NSFC for Distinguished Young Scholars with No. 61125205.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Pham, D.T., Karaboga, D.: Intelligent Optimisation Techniques. Springer, London (2000)
Zhan, Z.H., Li, J., Cao, J., Zhang, J., Chung, H., Shi, Y.H.: Multiple populations for multiple objectives: A coevolutionary technique for solving multiobjective optimization problems. IEEE Trans. Cybern. 43(2), 445–463 (2013)
Shen, M., Zhan, Z.H., Chen, W.N., Gong, Y.J., Zhang, J., Li, Y.: Bi-velocity discrete particle swarm optimization and its application to multicast routing problem in communication networks. IEEE Trans. Ind. Electron 61(12), 7141–7151 (2014)
Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)
Ting, C.K., Lee, C.N., Chang, H.C., Wu, J.S.: Wireless heterogeneous transmitter placement using multiobjective variable-length genetic algorithm. IEEE Trans. Systems, Man, and Cybernetics–Part B: Cybernetics 39(4), 945–958 (2009)
Li, Y.H., Zhan, Z.H., Lin, S., Zhang, J., Luo, X.N.: Competitive and cooperative particle swarm optimization with information sharing mechanism for global optimization problems. Information Sciences 239(1), 370–382 (2015)
Zhang, C., Zhan, Z.H.: Comparisons of selection strategy in genetic algorithm. Computer Engineering and Design 30(23), 5471–5478 (2009)
Zhu, G.P., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Applied Mathematics and Computing 217(7), 3166–3173 (2010)
Gao, W.F., Liu, S.Y., Huang, L.L.: A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Transaction on Cybernetics 43(3) (June 2013)
Banharnsakun, A., Achalakul, T., Sirinaovakul, B.: The best-so-far selection in artificial bee colony algorithm. Applied Soft Computing 11(2), 2888–2901 (2011)
Karaboga, N.: A new design method based on artificial bee colony algorithm for digital IIR filters. Journal of The Franklin Institute 346(4), 328–348 (2009)
Singh, A.: An Artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem. Applied Soft Computing 9(2), 625–631 (2009)
Rao, R.S., Narasimham, S., Ramalingaraju, M.: Optimization of distribution network configuration for loss reduction using artificial bee colony algorithm. In: Proc. International Conference on Advances in Mechanical Engineering, pp. 116–122 (2008)
Pan, Q.K., Tasgetiren, M.F., Suganthan, P.N., Chua, T.J.: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem. Information Sciences 181(12), 2455–2468 (2011)
Karaboga, D., Akay, B., Ozturk, C.: Artificial bee colony (ABC) optimization algorithm for training feed-forward neural networks. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds.) MDAI 2007. LNCS (LNAI), vol. 4617, pp. 318–329. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, MD., Zhan, ZH., Li, JJ., Zhang, J. (2014). Tournament Selection Based Artificial Bee Colony Algorithm with Elitist Strategy. In: Cheng, SM., Day, MY. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2014. Lecture Notes in Computer Science(), vol 8916. Springer, Cham. https://doi.org/10.1007/978-3-319-13987-6_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-13987-6_36
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13986-9
Online ISBN: 978-3-319-13987-6
eBook Packages: Computer ScienceComputer Science (R0)