Abstract
Particle swarm optimization (PSO) and artificial bee colony (ABC) are two formidable population-based optimizers inspired by swarm intelligence(SI). They follow different philosophies and paradigms, and both are successfully and widely applied in scientific and engineering research. The hybridization of PSO and ABC represents a promising way to create more powerful SI-based hybrid optimizers, especially for specific problem solving. In the past decade, numerous hybrids of ABC and PSO have emerged with diverse design ideas from many researchers. This paper is aimed at reviewing the existing hybrids based on PSO and ABC and giving a classification and an analysis of them.
This work was supported in part by the National Natural Science Foundation of China under Grant 61673058, U1609214 and 61304215, in part by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant 61321002, and in part by the Research Fund for the Doctoral Program of Higher Education of China under Grant 20131101120033.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Dorigo, M., Birattari, M.: Swarm intelligence. Scholarpedia 2(9), 1462 (2007)
Hinchey, M.G., Sterritt, R., Rouff, C.: Swarms and swarm intelligence. Computer 40(4), 111–113 (2007)
Andrea, R., Blesa, M., Blum, C., Michael, S.: Hybrid Metaheuristics-an Emerging Approach to Optimization. Springer, Heidelberg (2008)
Voß, S.: Hybridizing metaheuristics: the road to success in problem solving. In: Proceedings of the 8th EU/MEeting on Metaheuristics in the Service Industry, Stuttgart (2007)
Talbi, E.-G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8(5), 541–564 (2002)
Gendreau, M., Potvin, J.-Y.: Metaheuristics in combinatorial optimization. Ann. Oper. Res. 140(1), 189–213 (2005)
Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)
Raidl, G.R.: A unified view on hybrid metaheuristics. In: Almeida, F., Blesa Aguilera, M.J., Blum, C., Moreno Vega, J.M., Pérez Pérez, M., Roli, A., Sampels, M. (eds.) HM 2006. LNCS, vol. 4030, pp. 1–12. Springer, Heidelberg (2006). doi:10.1007/11890584_1
Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans. Evol. Comput. 9(5), 474–488 (2005)
Xin, B., Chen, J., Zhang, J., Fang, H., Peng, Z.-H.: Hybridizing differential evolution and particle swarm optimization to design powerful optimisers: a review and taxonomy. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(5), 744–767 (2012)
Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J.: Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 39(3), 680–691 (2009)
Jones, T.: One operator, one landscape, Santa Fe Institute Technical report, 95-02-025 (1995)
El-Abd, M.: A hybrid ABC-SPSO algorithm for continuous function optimization. In: IEEE Symposium on Swarm Intelligence (SIS), pp. 1–6. IEEE (2011)
Sharma, T.K., Pant, M., Bhardwaj, T.: PSO ingrained artificial bee colony algorithm for solving continuous optimization problems. In: 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE) (2011)
Shi, X., Li, Y., Li, H., Guan, R., Wang, L., Liang, Y.: An integrated algorithm based on artificial bee colony and particle swarm optimization. In: 2010 Sixth International Conference on Natural Computation (ICNC), vol. 5, pp. 2586–2590. IEEE (2010)
Altun, O., Korkmaz, T.: Particle swarm optimization-artificial bee colony chain (PSOABCC): a hybrid meteahuristic algorithm. In: Scientific Cooperations International Workshops on Electrical and Computer Engineering Subfields, pp. 22–23, August 2014
Muthiah, A., Rajkumar, A., Rajkumar, R.: Hybridization of artificial bee colony algorithm with particle swarm optimization algorithm for flexible job shop scheduling. In: Proceedings of 2016 International Conference on Energy Efficient Technologies for Sustainability (ICEETS), pp. 896–903. IEEE (2016)
Baktash, N., Meybodi, M.: A new hybridized approach of PSO and ABC for optimization. In: Proceedings of the 2011 International Conference on Measurement and Control Engineering, pp. 69–80 (2011)
Amudha, P., Karthik, S., Sivakumari, S.: A hybrid swarm intelligence algorithm for intrusion detection using significant features. Sci. World J. 2015, 15 (2015). doi:10.1155/2015/574589. Article ID 574589
Vitorino, L., Ribeiro, S., Bastos-Filho, C.J.: A mechanism based on artificial bee colony to generate diversity in particle swarm optimization. Neurocomputing 148, 39–45 (2015)
Li, Z., Wang, W., Yan, Y., Li, Z.: PS-ABC: a hybrid algorithm based on particle swarm and artificial bee colony for high-dimensional optimization problems. Expert Syst. Appl. 42(22), 8881–8895 (2015)
Alqattan, Z.N., Abdullah, R.: A hybrid artificial bee colony algorithm for numerical function optimization. Int. J. Mod. Phys. C 26(10), 1550109 (2015)
Chun-Feng, W., Kui, L., Pei-Ping, S.: Hybrid artificial bee colony algorithm and particle swarm search for global optimization. Math. Probl. Eng. 2014, 8 (2014). doi:10.1155/2014/832949. Article ID 832949
Bouaziz, S., Dhahri, H., Alimi, A.M., Abraham, A.: Evolving flexible beta basis function neural tree using extended genetic programming & hybrid artificial bee colony. Appl. Soft Comput. 47, 653–668 (2016)
Xiang, Y., Peng, Y., Zhong, Y., Chen, Z., Lu, X., Zhong, X.: A particle swarm inspired multi-elitist artificial bee colony algorithm for real-parameter optimization. Comput. Optim. Appl. 57(2), 493–516 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Xin, B., Wang, Y., Chen, L., Cai, T., Chen, W. (2017). A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony. In: Tan, Y., Takagi, H., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2017. Lecture Notes in Computer Science(), vol 10386. Springer, Cham. https://doi.org/10.1007/978-3-319-61833-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-61833-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61832-6
Online ISBN: 978-3-319-61833-3
eBook Packages: Computer ScienceComputer Science (R0)