Optimal simulated annealing method and its application to combinatorial problems

A simulated annealing method based on stochastic dynamic programming is proposed for obtaining a rapid convergence to a global minimum of multivariable optimization problems. A central concern is to provide appropriate temperature values that determine the convergence of the cooling schedule. Using the stochastic dynamic programming method, the cooling schedule is derived by minimizing the time that the system requires to reach the global minimum from an initial state, Monte Carlo simulation shows that the present schedule gives good near-optimal solutions.<>