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A New Filled Function Method with Two Parameters for Global Optimization

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Abstract

The filled function method is an effective approach to find the global minimizer of multi-modal functions. The conventional filled functions are often numerically unstable due to the exponential or logarithmic term and the sensitivity to parameters. In this paper, a new filled function is proposed, which is continuously differentiable, not sensitive to parameters, and not easy to cause overflow. Then a new local search algorithm is given. Based on this, a new filled function method is proposed. The simulations indicate that the proposed method is numerically stable to the variations of the initial points and the parameters. The comparison with some existing algorithms shows that the proposed method is more efficient and effective.

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Acknowledgments

The authors are grateful to the editor and the referees for their valuable comments and suggestions. This work is supported by The National Natural Science Foundation of China (No. 61272119), and The National Natural Science Foundation of China (No. 61203372).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xidian University, Xi’an, 710071, China

    Fei Wei, Yuping Wang & Hongwei Lin

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  1. Fei Wei
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  2. Yuping Wang
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  3. Hongwei Lin
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Correspondence to Yuping Wang.

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Wei, F., Wang, Y. & Lin, H. A New Filled Function Method with Two Parameters for Global Optimization. J Optim Theory Appl 163, 510–527 (2014). https://doi.org/10.1007/s10957-013-0515-1

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  • DOI: https://doi.org/10.1007/s10957-013-0515-1

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