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Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule
【2013.7.16 9:30-10:30am,S309】

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  Colloquia & Seminars 



   Prof. Faming Liang(Texas A&M University) 



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



    2013.7.16 9:30-10:30am                                      




    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 have so long CPU time. We propose 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, we show that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., 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..



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