Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one...

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Veröffentlicht in:Journal of the American Statistical Association Jg. 109; H. 506; S. 847 - 863
Hauptverfasser: Liang, Faming, Cheng, Yichen, Lin, Guang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Alexandria Taylor & Francis 01.06.2014
Taylor & Francis Group, LLC
Taylor & Francis Ltd
Schlagworte:
Algorithms
Annealing
Approximation
Competitors
Cooling
Energy value
Evolution
Local trap
Markov chain Monte Carlo
Mathematical minima
Monte Carlo simulation
Neural networks
Optimization
protein folding
Sampling distributions
Simulated annealing
Simulation
Statistics
Stochastic approximation Monte Carlo
Stochastic models
Stochastic optimization
temperature
Theory and Methods
Truncation
ISSN:1537-274X, 0162-1459, 1537-274X
Online-Zugang:Volltext
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Zusammenfassung:Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to use this much CPU time. This article proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, for example, a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors. Supplementary materials for this article are available online.
Bibliographie:http://dx.doi.org/10.1080/01621459.2013.872993
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ISSN:1537-274X
0162-1459
1537-274X
DOI:10.1080/01621459.2013.872993