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International Conference on Swarm Intelligence

ICSI 2017: Advances in Swarm Intelligence pp 242–249Cite as

A Review on Hybridization of Particle Swarm Optimization with Artificial Bee Colony

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10386))

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.

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References

  1. Dorigo, M., Birattari, M.: Swarm intelligence. Scholarpedia 2(9), 1462 (2007)

    Article  Google Scholar 

  2. Hinchey, M.G., Sterritt, R., Rouff, C.: Swarms and swarm intelligence. Computer 40(4), 111–113 (2007)

    Article  Google Scholar 

  3. Andrea, R., Blesa, M., Blum, C., Michael, S.: Hybrid Metaheuristics-an Emerging Approach to Optimization. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  4. 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)

    Google Scholar 

  5. Talbi, E.-G.: A taxonomy of hybrid metaheuristics. J. Heuristics 8(5), 541–564 (2002)

    Article  Google Scholar 

  6. Gendreau, M., Potvin, J.-Y.: Metaheuristics in combinatorial optimization. Ann. Oper. Res. 140(1), 189–213 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  7. Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. (CSUR) 35(3), 268–308 (2003)

    Article  Google Scholar 

  8. 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

    Chapter  Google Scholar 

  9. Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans. Evol. Comput. 9(5), 474–488 (2005)

    Article  Google Scholar 

  10. 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)

    Article  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. Jones, T.: One operator, one landscape, Santa Fe Institute Technical report, 95-02-025 (1995)

    Google Scholar 

  13. El-Abd, M.: A hybrid ABC-SPSO algorithm for continuous function optimization. In: IEEE Symposium on Swarm Intelligence (SIS), pp. 1–6. IEEE (2011)

    Google Scholar 

  14. 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)

    Google Scholar 

  15. 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)

    Google Scholar 

  16. 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

    Google Scholar 

  17. 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)

    Google Scholar 

  18. 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)

    Google Scholar 

  19. 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

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. Alqattan, Z.N., Abdullah, R.: A hybrid artificial bee colony algorithm for numerical function optimization. Int. J. Mod. Phys. C 26(10), 1550109 (2015)

    Article  MathSciNet  Google Scholar 

  23. 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

    Article  MathSciNet  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. 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)

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. School of Automation, Beijing Institute of Technology, Beijing, 100081, China

    Bin Xin, Yipeng Wang, Lu Chen, Tao Cai & Wenjie Chen

  2. Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing, 100081, China

    Bin Xin, Tao Cai & Wenjie Chen

  3. Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing, 100081, China

    Bin Xin

Authors
  1. Bin Xin
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  2. Yipeng Wang
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  3. Lu Chen
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  4. Tao Cai
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  5. Wenjie Chen
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Corresponding author

Correspondence to Bin Xin .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Kyushu University, Fukuoka, Japan

    Hideyuki Takagi

  3. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  4. Shenzhen University, Shenzhen, China

    Ben Niu

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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

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  • DOI: https://doi.org/10.1007/978-3-319-61833-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61832-6

  • Online ISBN: 978-3-319-61833-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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