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Artificial Bee Colony Based on Adaptive Selection Probability

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Artificial Intelligence Algorithms and Applications (ISICA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1205))

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Abstract

Because of the powerful searching ability of artificial bee colony algorithm, it has applications in various fields. However, it still has a drawback on local search ability. Therefore, an adaptive selection probability ABC algorithm (called PABC) is proposed to improve its local search ability. In the multi-strategy search solutions, a probability is assigned to each strategy and the probability is adaptive adjusted to control the choice of strategy. Meanwhile, a modified mean center is introduced to replace the global best solution to guide search. The proposed PABC is proved to have better optimization ability than some other improved ABCs by testing classical 12 functions.

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References

  1. Patel, N., Padhiyar, N.: Modified genetic algorithm using box complex method. Application to optimal 533 control problems. J. Process Control 26, 35–50 (2015)

    Article  Google Scholar 

  2. Meang, Z., Pan, J.S., Kong, L.P.: Parameters with adaptive learning mechanism (PALM) for the enhancement of differential evolution. Knowl.-Based Syst. 141, 92–112 (2018)

    Article  Google Scholar 

  3. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)

    Article  Google Scholar 

  4. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  5. Zhu, G., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217, 3166–3173 (2010)

    MathSciNet  MATH  Google Scholar 

  6. Cui, L.Z., et al.: A novel artificial bee colony algorithm with depth-first search framework and elite-guided search equation. Inf. Sci. 367(368), 1012–1044 (2016)

    Article  Google Scholar 

  7. Wang, Z.G., Shang, X.D., Xia, H.M., Ding, H.: Artificial bee colony algorithm with multi-search strategy cooperative evolutionary. Control Decis. 33(02), 235–241 (2018)

    Google Scholar 

  8. Wang, H., Wu, Z.J., Rahnamayan, S., Sun, H., Liu, Y., Pan, J.S.: Multi-strategy ensemble artificial bee colony algorithm. Inf. Sci. 279, 587–603 (2014)

    Article  MathSciNet  Google Scholar 

  9. Kiran, M.S., Hakli, H., Guanduz, M., Uguz, H.: Artificial bee colony algorithm with variable search strategy for continuous optimization. Inf. Sci. 300, 140–157 (2015)

    Article  MathSciNet  Google Scholar 

  10. Zhu, G.P., Kwong, S.: Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 217(7), 3166–3173 (2010)

    MathSciNet  MATH  Google Scholar 

  11. Gao, W.F., Liu, S.Y.: A modified artificial bee colony algorithm. Comput. Oper. Res. 39(3), 687–697 (2012)

    Article  Google Scholar 

  12. Sun, H., Deng, Z.C., Zhao, J., Wang, H., Xie, H.H.: Mixed mean center reverse learning particle swarm optimization algorithm. Electron. J. 47(09), 1809–1818 (2019)

    Google Scholar 

  13. Wang, H., et al.: Firefly algorithm with neighborhood attraction. Inf. Sci. 382, 374–387 (2017)

    Article  Google Scholar 

  14. Wang, H., Cui, Z.H., Sun, H., Rahnamayan, S., Yang, X.S.: Randomly attracted firefly algorithm with neighborhood search and dynamic parameter adjustment mechanism. Soft. Comput. 21, 5325–5339 (2017)

    Article  Google Scholar 

  15. Wang, H., Sun, H., Li, C.H., Rahnamayan, S., Pan, J.S.: Diversity enhanced particle swarm optimization with neighborhood search. Inf. Sci. 223, 119–135 (2013)

    Article  MathSciNet  Google Scholar 

  16. Gao, W.F., Liu, S.Y., Huang, L.L.: A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE Trans. Cybern. 43(3), 1011–1024 (2013)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 61663028).

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Authors and Affiliations

  1. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, 330099, China

    Songyi Xiao, Hui Wang & Minyang Xu

  2. School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China

    Songyi Xiao, Hui Wang & Minyang Xu

  3. School of Business Administration, Nanchang Institute of Technology, Nanchang, 330099, China

    Wenjun Wang

Authors
  1. Songyi Xiao
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  2. Hui Wang
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  3. Minyang Xu
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  4. Wenjun Wang
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Corresponding author

Correspondence to Hui Wang .

Editor information

Editors and Affiliations

  1. South China Agricultural University, Guangzhou, China

    Kangshun Li

  2. Jiangxi University of Science and Technology, Ganzhou, China

    Wei Li

  3. South China Agricultural University, Guangzhou, China

    Hui Wang

  4. The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

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Xiao, S., Wang, H., Xu, M., Wang, W. (2020). Artificial Bee Colony Based on Adaptive Selection Probability. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_2

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  • DOI: https://doi.org/10.1007/978-981-15-5577-0_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5576-3

  • Online ISBN: 978-981-15-5577-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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