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A derivative-free algorithm for unconstrained optimization

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Abstract

In this paper a hybrid algorithm which combines the pattern search method and the genetic algorithm for unconstrained optimization is presented. The algorithm is a deterministic pattern search algorithm, but in the search step of pattern search algorithm, the trial points are produced by a way like the genetic algorithm. At each iterate, by reduplication, crossover and mutation, a finite set of points can be used. In theory, the algorithm is globally convergent. The most stir is the numerical results showing that it can find the global minimizer for some problems, which other pattern search algorithms don't bear.

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Authors and Affiliations

  1. School of Math. Sci. and Comput. Tech., Central South Univ., 410075, Changsha, China

    Peng Yehui & Liu Zhenhai

  2. Dept. of Math., Huaihua College, 418008, Hunan, China

    Peng Yehui

Authors
  1. Peng Yehui
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  2. Liu Zhenhai
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Additional information

Supported by Scientific Research Fund of Hunan Province Education Committee (04C464) and by Huaihua College.

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Yehui, P., Zhenhai, L. A derivative-free algorithm for unconstrained optimization. Appl. Math. Chin. Univ. 20, 491–498 (2005). https://doi.org/10.1007/s11766-005-0029-1

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  • DOI: https://doi.org/10.1007/s11766-005-0029-1

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