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Image 1D OMP sparse decomposition with modified fruit-fly optimization algorithm

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Abstract

The Fruit-fly optimization algorithm (FOA) is good at parallel search ability in the evolution process, but it traps in local optimum sometimes. Simulated Annealing (SA) algorithm accepts the second-optimum solution with Mrtropolis criterion so as to jump out of the local optimum. So, combined the advantages of two algorithms, modified FOA (FOA-SA) algorithm is presented in this paper. In FOA-SA, the smell concentration function is improved as well, so as to get the whole searching directions for fruit-fly. At the same time, in order to solve the problem of the computational complexity in image 2D sparse decomposition, image 1D orthogonal matching pursuit (OMP) algorithm with FOA-SA algorithm is implemented. Experimental results show that the convergence of FOA-SA is better than that in FOA, and the speed of image 1D sparse algorithm is 2.41 times faster than 2D for the 512 \(\times \) 512 image under the same conditions.

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References

  1. Averbuch, Amir Z., Zheludev, Valery A., Khazanovsky, Marie: Deconvolution by matching pursuit using spline wavelet packets dictionaries. Appl. Comput. Harmonic Anal. 31(1), 98–124 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  2. Phillips, P.: Matching pursuit filter design. In: Proceedings of the 12th IAPR International Conference on Signal Processing, Jerusalem, Israel, 57–61 March 1994

  3. Ouyang, Z., Li, Y.: Omp-based multi-band signal reconstruction for ecological sounds recognition. J. Electron. 31(1), 50–60 (2014)

    Google Scholar 

  4. Guo, H.Y., Li, X.X., Zhou, L., Wu, Z.Y.: Single-channel speech separation using orthogonal matching pursuit. J. Softw. 9(11), 2974–2980 (2014)

    Google Scholar 

  5. Yin, Z.K., Xie, M., Wang, J.Y.: Image denoising base on its sparse decomposition. J. Univ. Electron. Sci. Technol. China 35(6), 876–878 (2006)

    Google Scholar 

  6. Czerepinski, P., Davies, C., Canagarajah, N., Bull, D.: Matching pursuits video coding: dictionaries and fast implementation. IEEE Trans. Circuits Syst. Video Technol. 10(7), 1103–1115 (2000)

    Article  Google Scholar 

  7. Zhang, C.J., Liu, J., Liang, C., Xue, Z., Pang, J.B., Huang, Q.M.: Image classification by non-negative sparse coding, correlation constrained low-rank and sparse decomposition. Comput. Vis. Image Underst. 123(6), 14–22 (2014)

    Article  Google Scholar 

  8. Yin, Z.K., Wang, J.Y., Pierre, V.: A fast algorithm for image reconstruction based on sparse decomposition. Front. Electr. Electron. Eng. China 2(4), 432–434 (2007)

    Article  Google Scholar 

  9. Yang, M., Chen, L.L.: OMP signal sparse decomposition with improved ACFOA. Comput. Eng. Appl. 51(20), 208–212 (2015)

    Google Scholar 

  10. Li, H.J., Yin, Z.K., Zhang, J.S., Wang, J.Y.: Image sparse decomposition based on particle swarm optimization with chaotic mutation. J. Southwest Jiaotong Univ. 43(4), 509–513 (2008)

    MATH  Google Scholar 

  11. Li, X.Y., Yin, Z.K.: Image sparse decomposition algorithm based on MP and 1D FFT. Comput. Sci. 37(10), 246–250 (2010)

    Google Scholar 

  12. Holland, J.H.: Building blocks, cohort genetic algorithms, and hyperplane-defined functions. Evol. Comput. 8(4), 373–391 (2000)

    Article  Google Scholar 

  13. Moghaddam, A., Behmanesh, J., Farsijani, A.: Parameters estimation for the new four-parameter nonlinear Muskingum model using the Particle Swarm Optimization. Water Resour. Manag. 30(7), 2143–2160 (2016)

    Article  Google Scholar 

  14. Shen, M.L., Li, L., Liu, D.: Research and application of function optimization based on Artificial Fish Swarm Algorithm. In: Proceedings of the 4th International Conference on Computer Engineering and Networks, Shanghai, China, 195–200 July 2014

  15. Tang, Z., Lu, Z.D., Li, R.X.: A routing algorithm for risk-scanning agents using ant colony algorithm in P2P network. Wuhan Univ. J. Nat. Sci. 11(5), 1097–1103 (2006)

    Article  MATH  Google Scholar 

  16. Liu, X., Chen, C., Zhao, Y.T., Wang, X.: Multi-wavelet decomposition and reconstruction based on matching pursuit algorithm fast optimized by particle swarm. J. Jilin Univ. 45(6), 1855–1861 (2015)

    Google Scholar 

  17. Liu, H., Wang, L.: On the application of MP sparse decomposition in image compression based on artificial fish swarm algorithm. J. Xi’an Univ. Arts Sci. 17(2), 74–77 (2014)

    Google Scholar 

  18. Liu, J.C., Guo, R., Qi, C.L.: Signal MP-based sparse decomposition with modified artificial bee colony algorithm. Techn. Autom. Appl. 35(5), 54–58 (2016)

    Google Scholar 

  19. Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26, 69–74 (2012)

    Article  Google Scholar 

  20. Ding, S.F., Zhang, X.K., Yu, J.Z.: Twin support vector machines based on fruit fly optimization algorithm. Int. J. Mach. Learn. Cybern. 7(2), 193–203 (2016)

    Article  Google Scholar 

  21. Yang, Y., Xu, Z., Liu, L., Sun, G.: A security carving approach for AVI video based on frame size and index. Multimed. Tools Appl. 76(3), 3293–3312 (2017)

    Article  Google Scholar 

  22. Pan, Q.K., Sang, H.Y., Duan, J.H., Gao, L.: An improved fruit fly optimization algorithm for continuous function optimization problems. Knowl.-Based Syst. 62, 69–83 (2014)

    Article  Google Scholar 

  23. Jiang, M., Cheng, Y.: Simulated annealing artificial fish swarm algorithm. In: Proceedings of IEEE 8th World Congress on Intelligent Control and Automation, Jinan, China, 1590–1593 July 2010

  24. Niknam, Taher, Amiri, Babak, Olamaei, Javad, Arefi, Ali: An efficient hybrid evolutionary optimization algorithm based on PSO and SA for clustering. J. Zhejiang Univ. Sci. A 10(4), 512–519 (2009)

    Article  MATH  Google Scholar 

  25. Li, C., Xu, S., Li, W., Hu, L.: A novel modified fly optimization algorithm for designing the self-tuning proportional integral derivative controller. J. Converg. Inf. Technol. 7(16), 69–77 (2012)

    Google Scholar 

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Acknowledgements

The research work is supported by “Twelfth Five-year” Scientific Research Program (No. [2013] 325) of Jilin Province Education Department of China.

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Authors and Affiliations

  1. College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, Jilin, China

    Ming Yang & Wei Liu

  2. Beijing Metstar Radar Co., LTD., Beijing, 100094, Beijing, China

    Ning-bo Liu

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  1. Ming Yang
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  2. Ning-bo Liu
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  3. Wei Liu
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Correspondence to Wei Liu.

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Yang, M., Liu, Nb. & Liu, W. Image 1D OMP sparse decomposition with modified fruit-fly optimization algorithm. Cluster Comput 20, 3015–3022 (2017). https://doi.org/10.1007/s10586-017-0966-5

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  • DOI: https://doi.org/10.1007/s10586-017-0966-5

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