ABSTRACT

Simulated annealing is a global optimization method that distinguishes
between different local optima. Starting from an initial point, the
algorithm takes a step and the function is evaluated. When minimizing a
function, any downhill step is accepted and the process repeats from this
new point. An uphill step may be accepted. Thus, it can escape from local
optima. This uphill decision is made by the Metropolis criteria. As the
optimization process proceeds, the length of the steps decline and the
algorithm closes in on the global optimum. Since the algorithm makes very
few assumptions regarding the function to be optimized, it is quite
robust with respect to non-quadratic surfaces. The degree of robustness
can be adjusted by the user. In fact, simulated annealing can be used as
a local optimizer for difficult functions.

This implementation of simulated annealing was used in "Global Optimizatio
of Statistical Functions with Simulated Annealing," Goffe, Ferrier and
Rogers, Journal of Econometrics, vol. 60, no. 1/2, Jan./Feb. 1994, pp.
65-100. Briefly, we found it competitive, if not superior, to multiple
restarts of conventional optimization routines for difficult optimization
problems.

For more information on this routine, contact its author:
Bill Goffe, bgoffe@whale.st.usm.edu

