1993 Volume 113 Issue 6 Pages 417-423
Discrete optimization problems are generally difficult to solve since they are NP-complete. They may have complex structure in solution space with huge number of local minima and the simulated annealing is well known as an effective stochastic algorithm for these problems. The determination of the cooling schedule is essential in annealing which is under the control of the critical temperature.
This paper discusses on the critical temperature in the neural net-like algorithm of the discrete quadratic optimization problems, derived through mean field approximation, and the validity of our evaluation method of the critical temperature is shown by many examples. It is also pointed out that the critical temperature can be made to be higher by modifying the structure of the objective function without changing the original discrete optimization problem. This derives the possibility of finding more efficient algorithms.