Cluster Computing

, Volume 22, Supplement 2, pp 3505–3512 | Cite as

Analysis of image processing algorithm based on bionic intelligent optimization

  • Yuetao DuEmail author
  • Nana Yang


In order to improve the speed and the accuracy of image segmentation, the nectar source fitness and defect nectar source replacement are improved through original artificial bee colony algorithm, in this paper, an optimized algorithm for combined use of artificial bee colony and Ostu is proposed. This method simulates the process of honey bee colony, and the threshold is regarded as nectar source, the fitness is regarded as the content of nectar source, then the segmentation of the image is completed successfully. In order to improve the speed of image segmentation, the long-term use of honey in original artificial bee colony is replaced with new nectar source, which can improve the running speed of the algorithm; In order to increase the accuracy of the algorithm and avoid local optimization, the fitness formula is meticulous through the fitness adjustment on nectar source. The experimental results show that the image segmentation reaches the ideal state, and the speed and precision of the segmentation are improved.


Artificial bee colony algorithm Optimization Nectar source fitness Defect nectar source replacement 


  1. 1.
    Kao, Y.T., Zahara, E., Kao, I.W.: A hybridized approach to data clustering [J]. Exp. Syst. Appl. 34(3), 1754–1762 (2008)Google Scholar
  2. 2.
    Ni, W., Gao, X., Wang, Y.: Single satellite image dehazing via linear intensity transformation and local property analysis [J]. Neurocomputing 175(Part 6), 25–39 (2016)Google Scholar
  3. 3.
    Minervini, M., Scharr, H., Tsaftaris, S.A.: The significance of image compression in plant phenotyping applications [J]. Funct. Plant Biol. 42(10), 1–43 (2015)Google Scholar
  4. 4.
    Ma, J., Fan, X., Ni, J., et al.: Multi-scale retinex with color restoration image enhancement based on Gaussian filtering and guided filtering [J]. Int. J. Mod. Phys. B 31, 1744077 (2017)Google Scholar
  5. 5.
    Du, G., Tian, S., Qiu, Y., et al.: Effective and efficient Grassfinch kernel for SVM classification and its application to recognition based on image set [J]. Chaos Solitons Fract 89(4), 295–303 (2016)Google Scholar
  6. 6.
    Min, H., Jia, W., Wang, X.F., et al.: An Intensity-Texture model based level set method for image segmentation [J]. Pattern Recog. 48(4), 1547–1562 (2015)Google Scholar
  7. 7.
    Bonab, M.B., Hashim, S.Z.M., Alsaedi, A.K.Z., et al.: Modified K-means combined with artificial bee colony algorithm and differential evolution for color image segmentation. In: Phon-Amnuaisuk, S., Au, T. (eds.) Computational Intelligence in Information Systems. Advances in Intelligent Systems and Computing, vol. 331, pp. 221–231. Springer, Cham (2015)Google Scholar
  8. 8.
    Karaboga, N.: A new design method based on artificial bee colony algorithm for digital IIR filters [J]. J. Frankl. Inst. 346(4), 328–348 (2009)Google Scholar
  9. 9.
    Dakshitha, B.A., Deekshitha, V., Manikantan, K.: A novel Bi-level artificial bee colony algorithm and its application to image segmentation [C]. In: IEEE International Conference on Computational Intelligence and Computing Research, pp. 1–7. IEEE (2016)Google Scholar
  10. 10.
    Huo, F., Liu, Y., Wang, D., et al.: Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation [J]. Signal Image Video Process. 12, 1–8 (2017)Google Scholar
  11. 11.
    Swietlicka, A.: Trained stochastic model of biological neural network used in image processing task [J]. Appl. Math. Comput. 267, 716–726 (2015)Google Scholar
  12. 12.
    El-Said, S.A.: Image quantization using improved artificial fish swarm algorithm [J]. Soft. Comput. 19(9), 2667–2679 (2015)Google Scholar
  13. 13.
    Wang, X., Fan, W., Xu, J.: An image edge detection method based on adaptive parallel ant colony optimization [J]. Tech. Bull. 55(5), 108–114 (2017)Google Scholar
  14. 14.
    Smith, J.E., Fogarty, T.C.: Operator and parameter adaptation in genetic algorithms [J]. Soft. Comput. 1(2), 81–87 (1997)Google Scholar
  15. 15.
    Zhuang, Y., Gao, K., Miu, X., et al.: Infrared and visual image registration based on mutual information with a combined particle swarm optimization—Powell search algorithm [J]. Opt. Int. J. Light Electron Opt. 127(1), 188–191 (2016)Google Scholar
  16. 16.
    Saxena, N., Mishra, K.: Improved multi-objective particle swarm optimization algorithm for optimizing watermark strength in color image watermarking [J]. Appl. Intell. 4, 1–20 (2017)Google Scholar
  17. 17.
    Dai, C., Chen, W., Zhu, Y., et al.: Reactive power dispatch considering voltage stability with seeker optimization algorithm [J]. Electr. Power Syst. Res. 79(10), 1462–1471 (2009)Google Scholar
  18. 18.
    Guvenc, U.: Active power loss minimization in electric power systems through artificial bee colony algorithm [J]. Int. Rev. Electr. Eng. 5(5), 2217–2223 (2010)Google Scholar
  19. 19.
    Karaboga, D, Gorkemli, B.A.: Combinatorial artificial bee colony algorithm for traveling salesman problem [C]. In: International Symposium on Innovations in Intelligent Systems and Applications, pp. 50–53. IEEE (2011)Google Scholar
  20. 20.
    Ma, M., Liang, J., Guo, M., et al.: SAR image segmentation based on artificial bee colony algorithm [J]. Appl. Soft Comput. 11(8), 5205–5214 (2011)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Electronic Information EngineeringXi’an Technological UniversityXi’anChina
  2. 2.School of Art and MediaXi’an Technological UniversityXi’anChina

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