Accelerated simulated tempering

We propose a new stochastic global optimization method by accelerating the simulated tempering scheme with random walks executed on a temperature ladder with various transition step sizes. By suitably choosing the length of the transition steps, the accelerated scheme enables the search process to execute large jumps and escape entrapment in local minima, while retaining the capability to explore local details, whenever warranted. Our simulations confirm the expected improvements and show that the accelerated simulated tempering scheme has a much faster convergence to the target distribution than Geyer and Thompson’s simulated tempering algorithm and exhibits accuracy comparable to the simulated annealing method.  2004 Elsevier B.V. All rights reserved.