Work

Unsatisfiability-based Algorithms for Multi-Objective Combinatorial Optimization

Year: 2026

Type: article

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 scarce. Due to the shortage of efficient tools for MOCO, many real-world applications formulated as multi-objective are cast as single-objective, using either a linear combination or by setting a preference order among the objectives. In this paper, we extend the state of the art of MOCO solvers with three novel unsatisfiability-based algorithms. The first two are core-guided MOCO solvers. The third is a hitting set MOCO solver. Experimental results in several sets of benchmark instances show that our new unsatisfiability-based algorithms can outperform and complement other SAT-based, state-of-the-art algorithms for MOCO.

Source: Journal of Automated Reasoning

DOI: 10.1007/s10817-026-09749-w

Authors: João Cortes, Inês Lynce, Vasco Manquinho

Institutions: Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento, University of Lisbon, Instituto Superior Técnico

Language: English

Topic: Constraint Satisfaction and Optimization

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: Universidade de Lisboa, Fundação para a Ciência e a Tecnologia