A Simulated Annealing Algorithm for the Satisfiability Problem Using Dynamic Markov Chains with Linear Regression Equilibrium

Since the appearance of Simulated Annealing algorithm it has shown to be an efficient method to solve combinatorial optimization problems such as Boolean Satisfiability problem. New algorithms based on two cycles: one external for temperatures and other internal, named Metropolis, have emerged. These algorithms usually use the sameMarkov chain length in the Metropolis cycle for each temperature. In this paper we propose a method based on linear regression to find the Metropolis equilibrium. Experimentation shows that the proposed method is more efficient than the classical one, since it obtains the same quality of the final solution with less processing time.