The Stochastics of Threshold Accepting: Analysis of an Application to the Uniform Design Problem
AbstractThreshold Accepting (TA) is a powerful optimization heuristic from the class of stochastic local search algorithms. It has been applied successfully to different optimization problems in statistics and econometrics, including the uniform design problem. Using the latter application as example, the stochastic properties of a TA implementation are analyzed. We provide a formal framework for the analysis of optimization heuristics like TA, which can be used to estimate lower bounds and to derive convergence results. It is also helpful for tuning real applications. Based on this framework, empirical results are presented for the uniform design problem. In particular, for two problem instances, the rate of convergence of the algorithm is estimated to be of the order of a power of -0.3 to -0.7 of the number of iterations. --
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Bibliographic InfoPaper provided by University of Erfurt, Faculty of Economics, Law and Social Sciences in its series Discussion Papers with number 2005,003E.
Date of creation: 2005
Date of revision:
Heuristic optimization; Threshold Accepting; Stochastic analysis of heuristics;
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- Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
- Dennis K.J. Lin & Chris Sharpe & Peter Winker, 2009.
"Optimized U-type Designs on Flexible Regions,"
- Peter Winker & Dietmar Maringer, 2009. "The convergence of estimators based on heuristics: theory and application to a GARCH model," Computational Statistics, Springer, vol. 24(3), pages 533-550, August.
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