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Performance of Differential Evolution Method in Least Squares Fitting of Some Typical Nonlinear Curves

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  • Mishra, SK

Abstract

No foolproof method exists to fit nonlinear curves to data or estimate the parameters of an intrinsically nonlinear function. Some methods succeed at solving a set of problems but fail at the others. The Differential Evolution (DE) method of global optimization is an upcoming method that has shown its power to solve difficult nonlinear optimization problems. In this study we use the DE to solve some nonlinear least squares problems given by the National Institute of Standards and Technology (NIST), US Department of Commerce, USA and some other challenge problems posed by the CPC-X Software (the makers of the AUTO2FIT software). The DE solves the test problems given by the NIST and most of the challenge problems posed by the CPC-X, doing marginally better than the AUTO2FIT software in a few cases.

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File URL: http://mpra.ub.uni-muenchen.de/4656/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 4634.

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Date of creation: 29 Aug 2007
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Handle: RePEc:pra:mprapa:4634

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Related research

Keywords: Nonlinear least squares; curve fitting; Differential Evolution; global optimization; AUTO2FIT; CPC-X Software; NIST; National Institute of Standards and Technology; test problems;

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  1. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
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Cited by:
  1. repec:ebl:ecbull:v:3:y:2007:i:51:p:1-13 is not listed on IDEAS
  2. Mishra, SK, 2007. "Least squares estimation of joint production functions by the Differential Evolution method of global optimization," MPRA Paper 4813, University Library of Munich, Germany.
  3. Mishra, SK, 2008. "Construction of composite indices in presence of outliers," MPRA Paper 8874, University Library of Munich, Germany.

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