New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization
Abstract
In the last decade, a plethora of algorithms for single-objective Boolean optimization has 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 obtained in a wide range of benchmark instances show that our new unsatisfiability-based algorithms can outperform state-of-the-art SAT-based algorithms for MOCO.
Cite
@article{arxiv.2204.10856,
title = {New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization},
author = {João Cortes and Inês Lynce and Vasco Manquinho},
journal= {arXiv preprint arXiv:2204.10856},
year = {2022}
}