Incremental Encoding of Pseudo-Boolean Goal Functions Based on Comparator Networks

Karpiński, Michał; Piotrów, Marek

doi:10.1007/978-3-030-51825-7_36

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Algorithms and Implementation1

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2020

2023

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Cited by 2 publications

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“…Additionally, an order encoding [29] is also used. We refer the interested reader to the literature for further details on this and other encodings [27,13,14,15]. Observe that any unary encoding from PB into CNF can be used.…”

Section: Algorithms and Implementationmentioning

confidence: 99%

New Core-Guided and Hitting Set Algorithms for Multi-Objective Combinatorial Optimization

Cortes

Lynce

Manquinho

2023

Lecture Notes in Computer Science

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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 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 in several sets of benchmark instances show that our new unsatisfiability-based algorithms can outperform state-of-the-art SAT-based algorithms for MOCO.

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Section: Algorithms and Implementationmentioning

confidence: 99%