Improving probability selection based weights for satisfiability problems
Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incompl...
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| Published in: | Knowledge-based systems Vol. 245; p. 108572 |
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| Main Authors: | , , , , |
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| Language: | English |
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07.06.2022
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| Abstract | Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively. |
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| AbstractList | Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively. |
| ArticleNumber | 108572 |
| Author | Xu, Yang Sutcliffe, Geoff Fu, Huimin Liu, Jun Wu, Guanfeng |
| Author_xml | – sequence: 1 givenname: Huimin orcidid: 0000-0002-4447-3585 surname: Fu fullname: Fu, Huimin email: fuhuimin@swufe.edu.cn organization: School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu 611130, China – sequence: 2 givenname: Jun surname: Liu fullname: Liu, Jun organization: School of Computing, Ulster University, Northern Ireland, UK – sequence: 3 givenname: Guanfeng surname: Wu fullname: Wu, Guanfeng email: wgf1024@swjtu.edu.cn organization: School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China – sequence: 4 givenname: Yang surname: Xu fullname: Xu, Yang organization: School of Mathematics, Southwest Jiaotong University, Chengdu 610031, China – sequence: 5 givenname: Geoff surname: Sutcliffe fullname: Sutcliffe, Geoff organization: Department of Computer Science, University of Miami, USA |
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