Please use this identifier to cite or link to this item:
Winker, Peter
Year of Publication: 
Series/Report no.: 
Discussion paper / Universität Erfurt, Staatswissenschaftliche Fakultät 2005,003E
Threshold 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.
Heuristic optimization
Threshold Accepting
Stochastic analysis of heuristics
Document Type: 
Working Paper

Files in This Item:
191.78 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.