No abstract

In the last decade, numerous algorithms for single-objective Boolean optimization have been proposed that rely on the iterative usage of a highly effective Propositional Satisfiability (SAT) solver. But the use of SAT solvers in Multi-Objective Combinatorial Optimization (MOCO) algorithms is still scarce. Due to this shortage of efficient tools for MOCO, many real-world applications formulated as multi-objective are simplified to single-objective, using either a linear combination or a lexicographic ordering of the objective functions to optimize.In this paper, we extend the state of the art of MOCO solvers with two novel unsatisfiability-based algorithms. The first is a core-guided MOCO solver. The second is a hitting set-based MOCO solver. Experimental results in several sets of benchmark instances show that our new unsatisfiability-based algorithms can outperform state-of-the-art SAT-based algorithms for MOCO.

No abstract