A Feedback Algorithm for Determining Search Parameters for Monte Carlo Optimization

Abstract

Monte Carlo methods have become popular for obtaining solutions to global optimization problems. One such Monte Carlo optimization technique is simulated annealing (SA). Typically in SA the parameters of the search are determineda priori. Using an aggregated, orlumped, version of SA's associated Markov chain and the concept of expected hitting time, we adjust the search parameters dynamically using information gained 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.