Negative Learning Ant Colony Optimization for MaxSAT

Abstract Recently, a new negative learning variant of ant colony optimization (ACO) has been used to successfully tackle range combinatorial problems. For providing stronger evidence the general applicability ACO, we investigate how it can be adapted solve Maximum Satisfiability problem (MaxSAT). The structure MaxSAT is different from problems considered date and there exists only few ACO approaches for MaxSAT. In this paper, describe three variants. They differ in way which sub-instances are solved at each algorithm iteration provide feedback main algorithm. addition using IBM ILOG CPLEX, two these variants use existing solvers purpose. experimental results show that proposed significantly outperform baseline as well CPLEX solvers. This result special interest because shows improve over by internally them smaller sub-instances.