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A Level-Value Estimation Method and Stochastic Implementation for Global Optimization

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Listed:
  • Zheng Peng

    (Fuzhou University)

  • Donghua Wu

    (Shanghai University)

  • Quan Zheng

    (Shanghai University)

Abstract

In this paper, we propose a new method, namely the level-value estimation method, for finding global minimizer of continuous optimization problem. For this purpose, we define the variance function and the mean deviation function, both depend on a level value of the objective function to be minimized. These functions have some good properties when Newton’s method is used to solve a variance equation resulting by setting the variance function to zero. We prove that the largest root of the variance equation equals the global minimal value of the corresponding optimization problem. We also propose an implementable algorithm of the level-value estimation method where importance sampling is used to calculate integrals of the variance function and the mean deviation function. The main idea of the cross-entropy method is used to update the parameters of sample distribution at each iteration. The implementable level-value estimation method has been verified to satisfy the convergent conditions of the inexact Newton method for solving a single variable nonlinear equation. Thus, convergence is guaranteed. The numerical results indicate that the proposed method is applicable and efficient in solving global optimization problems.

Suggested Citation

  • Zheng Peng & Donghua Wu & Quan Zheng, 2013. "A Level-Value Estimation Method and Stochastic Implementation for Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 156(2), pages 493-523, February.
    • Handle: RePEc:spr:joptap:v:156:y:2013:i:2:d:10.1007_s10957-012-0151-1
    DOI: 10.1007/s10957-012-0151-1
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    References listed on IDEAS

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    1. Reuven Rubinstein, 1999. "The Cross-Entropy Method for Combinatorial and Continuous Optimization," Methodology and Computing in Applied Probability, Springer, vol. 1(2), pages 127-190, September.
    2. Dirk P. Kroese & Sergey Porotsky & Reuven Y. Rubinstein, 2006. "The Cross-Entropy Method for Continuous Multi-Extremal Optimization," Methodology and Computing in Applied Probability, Springer, vol. 8(3), pages 383-407, September.
    3. Chelouah, Rachid & Siarry, Patrick, 2000. "Tabu Search applied to global optimization," European Journal of Operational Research, Elsevier, vol. 123(2), pages 256-270, June.
    4. W. X. Zhu, 2008. "Dynamic Globally Concavized Filled Function Method for Continuous Global Optimization," Journal of Optimization Theory and Applications, Springer, vol. 139(3), pages 635-648, December.
    5. Jiaqiao Hu & Michael C. Fu & Steven I. Marcus, 2007. "A Model Reference Adaptive Search Method for Global Optimization," Operations Research, INFORMS, vol. 55(3), pages 549-568, June.
    6. Pieter-Tjerk de Boer & Dirk Kroese & Shie Mannor & Reuven Rubinstein, 2005. "A Tutorial on the Cross-Entropy Method," Annals of Operations Research, Springer, vol. 134(1), pages 19-67, February.
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    Cited by:

    1. Zheng Peng & Donghua Wu & Wenxing Zhu, 2016. "The robust constant and its applications in random global search for unconstrained global optimization," Journal of Global Optimization, Springer, vol. 64(3), pages 469-482, March.

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