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

  • Haidong Hu
  • Chi-Man Pun
  • Ye Liu
  • Xiangjing Lai
  • Zeyu Yang
  • Hao GaoEmail author
Article
  • 38 Downloads

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.

Keywords

Image registration Artificial bee colony Logistic chaotic Convergence rate Mutual information 

Notes

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).

References

  1. 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 31:635–672MathSciNetCrossRefGoogle Scholar
  2. 2.
    Ayatollahi F, Shokouhi SB, Ayatollahi A (2012) A new hybrid particle swarm optimization for multimodal brain image registration. J Biomedical Science and Engineering 5:153–161CrossRefGoogle Scholar
  3. 3.
    Babaoglu I (2015) Artificial bee colony algorithm with distribution-based update rule. App. Soft. Comput. 34:851–861CrossRefGoogle Scholar
  4. 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 367-368:1012–2044Google Scholar
  5. 5.
    Cui LZ, Zhang K, Li GH (2017) Modified gbest-guided artificial bee colony algorithm with new probability model. Soft Comput 1:1–27Google Scholar
  6. 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):1453–1462CrossRefGoogle Scholar
  7. 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):1011–1024Google Scholar
  8. 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):2045–2055CrossRefGoogle Scholar
  9. 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. 10.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, TurkeyGoogle Scholar
  11. 11.
    Karaboga D, Gorkemli B (2014) A quick artificial bee colony (qABC) algorithm and its performance on optimization problems. App. Soft. Comput. 23:227–238CrossRefGoogle Scholar
  12. 12.
    Kıran MS, Fındık O (2015) A directed artificial bee colony algorithm. App. Soft. Comput. 26:454–462CrossRefGoogle Scholar
  13. 13.
    Kiran MS, Hakli H, Gunduz M et al (2015) Artificial bee colony algorithm with variable search strategy for continuous optimization. Inform Sciences 300:140–157MathSciNetCrossRefGoogle Scholar
  14. 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.: 235–241Google Scholar
  15. 15.
    Li X, Yang G (2016) Artificial bee colony algorithm with memory. App. Soft. Comput. 41:362–372CrossRefGoogle Scholar
  16. 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: 2524–2531Google Scholar
  17. 17.
    Lu H, Li Y, Uemura T, Ge Z, Xu X (2017) FDCNet: filtering deep convolutional network for marine organism classification. Multimedia Tools & Application 2:1–14Google Scholar
  18. 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):187–198CrossRefGoogle Scholar
  19. 19.
    May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261(5560):459–467CrossRefGoogle Scholar
  20. 20.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. IEEE World Congress on Computational Intelligence. IEEE, Alaska 69–73Google Scholar
  21. 21.
    Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comp 12(6):702–713CrossRefGoogle Scholar
  22. 22.
    Song X, Yan Q, Zhao M (2017) An adaptive artificial bee colony algorithm based on objective function value information. App Soft Comput 55:384–401CrossRefGoogle Scholar
  23. 23.
    Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359MathSciNetCrossRefGoogle Scholar
  24. 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): 289–301Google Scholar
  25. 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 213:191–203CrossRefGoogle Scholar
  26. 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):2494MathSciNetCrossRefGoogle Scholar
  27. 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. 28.
    Yao X, Liu Y (1999) Evolutionary programming made faster. IEEE Trans Evol Comput 3:82–102CrossRefGoogle Scholar
  29. 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):2935–2952Google Scholar
  30. 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: 568–572Google Scholar
  31. 31.
    Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Institute of Control EngineeringBeijingChina
  2. 2.Department of Computer and Information ScienceUniversity of MacauMacauChina
  3. 3.Nanjing University of Posts and TelecommunicationsNanjingChina

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