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Global Optimization Algorithms Using Fourier Smoothing

  • Conference paper
Intelligent Computing (ICIC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4113))

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Abstract

In this paper, a novel technique called Fourier smoothing technique, which can be used to improve any global optimization algorithm, is presented. This technique uses a properly truncated Fourier series as the smoothing function to approximate the objective function. This smoothing function can maintain the overall shape or basic shape of the objective function but eliminate its finer details. Thus it can eliminate many local minima but preserve the global minima, and make the search of optimal solution more easier and faster. To demonstrate efficiency of this technique, we integrate this technique into a simple optimization algorithm: Powell direct method. The simulation results indicate this smoothing technique can improve the Powell direct method greatly.

This work was supported by the National Natural Science Foundation of China (60374063), and SRF for ROCS, SEM.

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© 2006 Springer-Verlag Berlin Heidelberg

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Wang, Y. (2006). Global Optimization Algorithms Using Fourier Smoothing. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_108

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  • DOI: https://doi.org/10.1007/11816157_108

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37271-4

  • Online ISBN: 978-3-540-37273-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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