Movatterモバイル変換

[0]ホーム
URL:

Skip to main content
Springer Nature Link
Log in

An artificial bee algorithm with a leading group and its application into image registration

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

A popular optimization algorithm, the artificial bee colony algorithm (ABC), has attracted great attention over the recent years for its powerful global search ability. However, its slow convergence rate limits its development. In this paper, to further enhance its performance, we first introduced a new concept of a leading group, which includes some individuals with excellent performance, into the traditional ABC. The updated bee then selects one individual from the group to follow, which accelerates the convergence rate of the population. Furthermore, to enable the ABC algorithm to acquire greater opprotunities to search within a larger space, a logistic chaotic operator was introduced into our algorithm to balance its global and local search abilities. The performance of the algorithm proposed is tested on the traditional 12 benchmark functions and an image registration problem. The results reveal that our algorithm provides more acceptable results compared with the other algorithms.

This is a preview of subscription content,log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Ali MM, Khompatraporn C, Zabinsky ZB (2005) A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. J Glob Optim 31:635–672

    Article MathSciNet  Google Scholar 

  2. Ayatollahi F, Shokouhi SB, Ayatollahi A (2012) A new hybrid particle swarm optimization for multimodal brain image registration. J Biomedical Science and Engineering 5:153–161

    Article  Google Scholar 

  3. Babaoglu I (2015) Artificial bee colony algorithm with distribution-based update rule. App. Soft. Comput. 34:851–861

    Article  Google Scholar 

  4. Cui LZ, Li GH, Li Z et al (2016) A novel artificial bee colony algorithm with depth-first search framework and elite-guided search eqaution. Inform. Sciences 367-368:1012–2044

    Google Scholar 

  5. Cui LZ, Zhang K, Li GH (2017) Modified gbest-guided artificial bee colony algorithm with new probability model. Soft Comput 1:1–27

    Google Scholar 

  6. Falco ID, Cioppa AD, Maisto D, Tarantino E (2008) Differential evolution as a viable tool for satellite image registration. Appl Soft Comput 8(4):1453–1462

    Article  Google Scholar 

  7. Gao WF, Liu S, Huang L (2013) A novel artificial bee colony algorithm based on modified search equation and orthogonal learning. IEEE T Syst Man Cybern 43(3):1011–1024

    Google Scholar 

  8. Gao L, Guo Z, Zhang H, Xu X, Shen HT (2017) Video captioning with attention-based lstm and semantic consistency. IEEE Trans on Multimedia 19(9):2045–2055

    Article  Google Scholar 

  9. Huimin L, Bin L, Junwu Z et al (2017) Wound intensity correction and segmentation with convolutional neural networks. Concurrency and Computation: Practice and Experience.https://doi.org/10.1002/cpe.3927

  10. Karaboga D (2005)An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Turkey

  11. Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. App. Soft. Comput. 23:227–238

    Article  Google Scholar 

  12. Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. App. Soft. Comput. 26:454–462

    Article  Google Scholar 

  13. Kiran MS, Hakli H, Gunduz M et al (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inform Sciences 300:140–157

    Article MathSciNet  Google Scholar 

  14. Kuang F, Jin Z, Xu W, et al. (2014) A novel chaotic artificial bee colony algorithm based on tent map. in Proc. IEEE Congr. Evol. Comput.: 235–241

  15. Li X, Yang G (2016) Artificial bee colony algorithm with memory. App. Soft. Comput. 41:362–372

    Article  Google Scholar 

  16. Li X, Yang G, Kıran MS (2016) Search experience-based search adaptation in artificial bee colony algorithm. IEEE Congress on Evolutionary Computation (CEC’2016), IEEE, Vancouver: 2524–2531

  17. Lu H, Li Y, Uemura T, Ge Z, Xu X (2017) FDCNet: filtering deep convolutional network for marine organism classification. Multimedia Tools & Application 2:1–14

    Google Scholar 

  18. Mases F, Collgnon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16(2):187–198

    Article  Google Scholar 

  19. May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261(5560):459–467

    Article  Google Scholar 

  20. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. IEEE World Congress on Computational Intelligence. IEEE, Alaska 69–73

  21. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comp 12(6):702–713

    Article  Google Scholar 

  22. Song X, Yan Q, Zhao M (2017) An adaptive artificial bee colony algorithm based on objective function value information. App Soft Comput 55:384–401

    Article  Google Scholar 

  23. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article MathSciNet  Google Scholar 

  24. Wachowiak M P, Smolikova R, Z. Y F, Zurada J M and Elmaghraby A S (2004) An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans Evol Comput 8 (3): 289–301

  25. Xu X, He L, Sshimada A, Taniguchi RI, Lu H (2016) Learning unified binary codes for cross-modal retrieval via latent semantic hashing. Neurocomputing 213:191–203

    Article  Google Scholar 

  26. Xu X, Shen F, Yang Y, Shen HT, Li X (2017) Learning discriminative binary codes for large-scale cross-modal retrieval. IEEE Trans Image Process 26(5):2494

    Article MathSciNet  Google Scholar 

  27. Xu X, He L, Lu H, Gao L, Ji Y (2018) Deep adversarial metric learning for cross-modal retrieval. World Wide Web.https://doi.org/10.1007/s11280-018-0541-x

  28. Yao X, Liu Y (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102

    Article  Google Scholar 

  29. Yue X, Jiang JM, Zhao BP, Ma TH (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput 22(9):2935–2952

    Google Scholar 

  30. Zhang Q, Wen G J, Zhang C X, Lin Z R, Shang Z M, Wang H M (2014) Image registration with position and similarity constraints based on genetic algorithm. 2014 10th International Conference on Natural Computation (ICNC’2014), IEEE, Xiamen: 568–572

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

    MathSciNet MATH  Google Scholar 

Download references

Acknowledgments

This work was partially supported by the National Nature Science Foundation of China (No. 61571236, 61690210, 61690215), the Research Committee of University of Macau (MYRG2015-00011-FST, MYRG2018-00035-FST), the Science and Technology Development Fund of Macau SAR under Grant 041-2017-A1, Funded by Science and Technology on Space Intelligent Control Laboratory, No. KGJZDSYS-2018-02, Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX18_0300, KYCX18_0929).

Author information

Authors and Affiliations

  1. Beijing Institute of Control Engineering, Beijing, China

    Haidong Hu

  2. Department of Computer and Information Science, University of Macau, Macau, China

    Chi-Man Pun

  3. Nanjing University of Posts and Telecommunications, Nanjing, China

    Ye Liu, Xiangjing Lai, Zeyu Yang & Hao Gao

Authors
  1. Haidong Hu

    You can also search for this author inPubMed Google Scholar

  2. Chi-Man Pun

    You can also search for this author inPubMed Google Scholar

  3. Ye Liu

    You can also search for this author inPubMed Google Scholar

  4. Xiangjing Lai

    You can also search for this author inPubMed Google Scholar

  5. Zeyu Yang

    You can also search for this author inPubMed Google Scholar

  6. Hao Gao

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence toHao Gao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, H., Pun, CM., Liu, Y.et al. An artificial bee algorithm with a leading group and its application into image registration.Multimed Tools Appl79, 14643–14669 (2020). https://doi.org/10.1007/s11042-019-7211-6

Download citation

Keywords

Access this article

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Advertisement

[8]ページ先頭
©2009-2025 Movatter.jp