Modified extremal optimization for the hard maximum satisfiability problem

Abstract

Based on our recent study on probability distributions for evolution in extremal optimization (EO), we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) problem, a generalized version of the satisfiability (SAT) problem. The basic idea behind EOSAT is to generalize the evolutionary probability distribution in the Bose-Einstein-EO (BE-EO) algorithm, competing with other popular algorithms such as simulated annealing and WALKSAT. Experimental results on the hard MAXSAT instances from SATLIB show that the modified algorithms are superior to the original BE-EO algorithm.