Following the successful use of Propositional Satisfiability (SAT) algorithms in Boolean optimization (e.g., Maximum Satisfiability), several SAT-based algorithms have been proposed for Multi-Objective Combinatorial Optimization (MOCO). However, these new algorithms either provide a small subset of the Pareto front or follow a more exploratory search procedure and the solutions found are usually distant from the Pareto front. We extend the state of the art with a new SAT-based MOCO solver, Slide and Drill (Slide&Drill), that hones an upper bound set of the exact solution. Moreover, we show that Slide&Drill neatly complements proposed UNSAT-SAT algorithms for MOCO. These algorithms can work in tandem over the same shared "blackboard" formula, in order to enable a faster convergence. Experimental results in several sets of benchmark instances show that Slide&Drill can outperform other SAT-based algorithms for MOCO, in particular when paired with previously proposed UNSAT-SAT algorithms.
@InProceedings{cortes_et_al:LIPIcs.CP.2024.8, author = {Cortes, Jo\~{a}o and Lynce, In\^{e}s and Manquinho, Vasco}, title = {{Slide\&Drill, a New Approach for Multi-Objective Combinatorial Optimization}}, booktitle = {30th International Conference on Principles and Practice of Constraint Programming (CP 2024)}, pages = {8:1--8:17}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-336-2}, ISSN = {1868-8969}, year = {2024}, volume = {307}, editor = {Shaw, Paul}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2024.8}, URN = {urn:nbn:de:0030-drops-206932}, doi = {10.4230/LIPIcs.CP.2024.8}, annote = {Keywords: Multi-Objective Combinatorial Optimization, Satisfiability Algorithms} }
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