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Slide&Drill, a New Approach for Multi-Objective Combinatorial Optimization

Authors João Cortes , Inês Lynce , Vasco Manquinho

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Author Details

João Cortes
  • INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal
Inês Lynce
  • INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal
Vasco Manquinho
  • INESC-ID, Instituto Superior Técnico, Universidade de Lisboa, Portugal

Funding

This work was supported by Portuguese national funds through FCT, under projects UIDB/50021/2020 (DOI: 10.54499/UIDB/50021/2020), PTDC/CCI-COM/2156/2021 (DOI: 10.54499/PTDC/CCI-COM/2156/2021) and 2022.03537.PTDC (DOI: 10.54499/2022.03537.PTDC).

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João Cortes, Inês Lynce, and Vasco Manquinho. Slide&Drill, a New Approach for Multi-Objective Combinatorial Optimization. In 30th International Conference on Principles and Practice of Constraint Programming (CP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 307, pp. 8:1-8:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.CP.2024.8

Abstract

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.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Optimization algorithms
Keywords
  • Multi-Objective Combinatorial Optimization
  • Satisfiability Algorithms

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