Stochastic local search for Partial Max-SAT: an experimental evaluation

Alkasem, Haifa Hamad; Menaï, Mohamed El Bachir

doi:10.1007/s10462-020-09908-4

Cited by 8 publications

(7 citation statements)

References 88 publications

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“…The hard random satisfiability (HRS) is particularly interesting because it turns out to be one of the hardest for all SLS solvers [8,9]. The satisfiable hard random instances are generated by planting a solution using the Clause Distribution Control approach [10], and thus these instances are named as HRS-based instances.…”

Section: Preliminariesmentioning

confidence: 99%

“…The word "hard" is just saying such instances are hard for existing local search algorithms to solve. The author [8] has indicated that HRS problems have some potential in applications (e.g., generating HRS problems with a planted solution can be used in cryptography). HRS was added for the first time to the random track of SAT Competition in 2016 to evaluate and improve SAT solvers, especially for SLS solvers.…”

Section: Preliminariesmentioning

confidence: 99%

“…To make the experimental evaluation more comprehensive, apart from the existing URS benchmarks from the latest SAT Competitions (2016 and 2017), additional URS instances are generated according to the URS generator 8 , with additional application instances from SAT Race 2019. Specially, we adopt the following benchmarks:…”

Section: The Benchmarks and Experiments Preliminariesmentioning

confidence: 99%

“…The propositional satisfiability (SAT) problem is one of the most widely studied NP-complete problems and plays an outstanding role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications [8]. The SAT problem is fundamental in solving many practical problems [12] in combinatorial optimization, statistical physics, and circuit verification.…”

Section: Introductionmentioning

confidence: 99%

“…SLS algorithms are often evaluated on RS benchmarks [1], including uniform random k-SAT (URS) benchmarks and hard random SAT (HRS) benchmarks. We adopt the name of "hard random SAT" from [24] and the SAT competitions [8,9], which are random instances generated by planting a solution using an approach named "clause distribution control" (HRS) [10]. So, they are essentially HRS-based random instances.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Emphasis on the flipping variable: Towards effective local search for hard random satisfiability

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Section: Preliminariesmentioning

confidence: 99%

Section: Preliminariesmentioning

confidence: 99%

Section: The Benchmarks and Experiments Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emphasis on the flipping variable: Towards effective local search for hard random satisfiability

et al. 2021

Information Sciences

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Towards efficient local search for the minimum total dominating set problem

Liu

Wang

et al. 2021

Appl Intell

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Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set D of vertices such that every vertex not in D is adjacent to at least one vertex in D. The MinDS problem is a classic N P-hard problem and has been extensively studied because of its many disparate applications in network analysis. To solve this problem efficiently, many heuristic approaches have been proposed to obtain a good solution within an acceptable time limit. However, existing MinDS heuristic algorithms are always limited by various tie-breaking cases when selecting vertices, which slows down the effectiveness of the algorithms. In this paper, we design an efficient local search algorithm for the MinDS problem, named DmDS-a dual-mode local search framework that probabilistically chooses between two distinct vertex-swapping schemes. We further address limitations of other algorithms by introducing vertex selection criterion based on the frequency of vertices added to solutions to address tie-breaking cases, and a new strategy to improve the quality of the initial solution via a greedy-based strategy integrated with perturbation. We evaluate DmDS against the state-of-the-art algorithms on seven datasets, consisting of 346 instances (or families) with up to tens of millions of vertices. Experimental results show that DmDS obtains the best performance in accuracy for almost all instances and finds much better solutions than state-of-the-art MinDS algorithms on a broad range of large real-world graphs.

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Improving two-mode algorithm via probabilistic selection for solving satisfiability problem

Fu,

Cai,

et al. 2024

Information Sciences

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Stochastic local search for Partial Max-SAT: an experimental evaluation

Cited by 8 publications

References 88 publications

Emphasis on the flipping variable: Towards effective local search for hard random satisfiability

Emphasis on the flipping variable: Towards effective local search for hard random satisfiability

Towards efficient local search for the minimum total dominating set problem

Improving two-mode algorithm via probabilistic selection for solving satisfiability problem

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