2026-07-23T00:29:31Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:16703 2024-03-06T10:58:06Z ddc:004 open_access

SAT-Based Leximax Optimisation Algorithms Cabral, Miguel Janota, Mikoláš Manquinho, Vasco Multi-Objective Optimisation Leximax Sorting Networks In several real-world problems, it is often the case that the goal is to optimise several objective functions. However, usually there is not a single optimal objective vector. Instead, there are many optimal objective vectors known as Pareto-optima. Finding all Pareto-optima is computationally expensive and the number of Pareto-optima can be too large for a user to analyse. A compromise can be made by defining an optimisation criterion that integrates all objective functions. In this paper we propose several SAT-based algorithms to solve multi-objective optimisation problems using the leximax criterion. The leximax criterion is used to obtain a Pareto-optimal solution with a small trade-off between the objective functions, which is suitable in problems where there is an absence of priorities between the objective functions. Experimental results on the Multi-Objective Package Upgradeability Optimisation problem show that the SAT-based algorithms are able to outperform the Integer Linear Programming (ILP) approach when using non-commercial ILP solvers. Additionally, experimental results on selected instances from the MaxSAT evaluation adapted to the multi-objective domain show that our approach outperforms the ILP approach using commercial solvers. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Miguel Cabral and Mikoláš Janota and Vasco Manquinho 2022 Is Part Of LIPIcs, Volume 236, 25th International Conference on Theory and Applications of Satisfiability Testing (SAT 2022) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SAT.2022.29 urn:nbn:de:0030-drops-167030 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2022.29 eng https://creativecommons.org/licenses/by/4.0/legalcode