A FEEDBACK ALGORITHM FOR DETERMINING SEARCH PARAMETERS FOR MONTE-CARLO OPTIMIZATION

Monte Carlo methods have become popular for obtaining solutions to glo bal optimization problems. One such Monte Carlo optimization technique is simulated annealing (SA). Typically in SA the parameters of the se arch are determined a priori. Using an aggregated, or lumped, version of SA's associated Markov chain and the concept of expected hitting ti me, we adjust the search parameters dynamically using information gain ed from the SA search process. We present an algorithm that varies the SA search parameters dynamically, and show that, on average, dynamic adjustment of the parameters attains better solutions on a set of test problems than those attained with a logarithmic cooling schedule. (C) 1998 Academic Press.