Research of parallel evolutionary algorithms and its application in image processing

Junhui Zheng

Abstract


Evolutionary computation is the cross-cutting and frontier research field of information science, automation science and computer science. It is also the research direction of computational intelligence. In this paper, based on the evolutionary computation theory, a series model of evolutionary computation is established and the convergence of typical evolutionary algorithms is analyzed. The adaptive evolutionary algorithm based on population information entropy and its application in image is proposed. The paper proposes a decision-making algorithm based on bit-coded discernible matrix and its application in image analysis. The idea of parallel computing is applied to the problem of SAR image change detection and image feature extraction. The corresponding parallel computing framework is designed to improve the running speed of the algorithm. Experimental results showed that the proposed algorithm can improve the accuracy and processing speed of the algorithm, which satisfies the practical application requirements.


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References


ChenH.Y. (2014). The Research of Computer complex network reliability evaluation method based on GABP algorithm. In Applied Mechanics and Materials, 556,6207-6210.

KimM.C.,Park J.Y., Jung W., Kim H., Kim Y.J. (2010). Development of a standard communication protocol for an emergency situation management in nuclear power plants, Annals of Nuclear Energy,37(6), 888-893.

KimM.K. (2015). Short-term price forecasting of Nordic power market by combination Levenberg–Marquardt and Cuckoo search algorithms, IET Generation, Transmission & Distribution, 9(12), 1552-1562.

Li J., An X., Xu X. (2013). Visula saliency based on scale-space analysis in the frequency domain, IEEE Trans. Pattern Anal. Mach. Intell, 35, 996-1010.

Li W. (2009). Saliency-based automatic target detection in forward looking infrared images, Proc. IEEE Conf. Image Process, 2009(10), 957-960.

Lu R., Mucaki E.J., Rogan P.K. (2016). Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy Minimization, bioRxiv, 2016(6)), 042752.

PintoF.S., da CruzN.F.,Marques R.C. (2015). Contracting water services with public and private partners: a case study approach. Journal of Water Supply, Research and Technology-Aqua, 64(2), 194-210.

Qi S.X., Ma J., Li H., Zhang S. (2014). Infrared small target enhancement via phase spectrum of Quaternion Fourier Transform, Infrared Physics & Technology, 62, 50-58.

WallaceM.L.,RafolsI. (2015). Research Portfolio Analysis in Science Policy: Moving from Financial Returns to Societal Benefits, Minerva, 52(2), 79-115.

WuJ.X., WangT., SuoZ.Y. (2009). DOA estimation for ULA by spectral Capon rooting method, Electronics Letters,45(1), 84-85.

XiaoL., WangJ., YangX., XiaoL. (2015). A hybrid model based on data preprocessing for electrical power forecasting, International Journal of Electrical Power & Energy Systems, 64, 211-227.

Xu Y., Zhao Y., Jin C. (2010). Salient target detection based on pseudo-Wigner-Ville distribution and Renyi entropy, Opt. Lett., 35, 475-477.


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Revista de la Facultad de Ingeniería,

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