›› 2013,Vol. 28 ›› Issue (2): 247-254.doi: 10.1007/s11390-013-1326-4

• Special Section on Selected Paper from NPC 2011 • 上一篇    下一篇


Wen-Sheng Guo1 (郭文生), Member, CCF, ACM, Guo-Wu Yang (杨国武)1, Member, CCF, ACM William N. N. Hung2, Senior Member, IEEE, and Xiaoyu Song3, Senior Member, IEEE   

  • 收稿日期:2012-04-24 修回日期:2012-11-01 出版日期:2013-03-05 发布日期:2013-03-05

Complete Boolean Satisfiability Solving Algorithms Based on Local Search

Wen-Sheng Guo1 (郭文生), Member, CCF, ACM, Guo-Wu Yang (杨国武)1, Member, CCF, ACM William N. N. Hung2, Senior Member, IEEE, and Xiaoyu Song3, Senior Member, IEEE   

  1. 1 School of Computer Science and Engineering, University of Electronic Science and Technology of ChinaChengdu 611731, China;
    2 Synopsys Inc., Mountain View, California 94043, U.S.A.;
    3 Department of Electrical and Computer Engineering, Portland State University, Portland 97207, U.S.A.
  • Received:2012-04-24 Revised:2012-11-01 Online:2013-03-05 Published:2013-03-05
  • Supported by:

    This work was partially supported by the National Natural Science Foundation of China under Grant Nos. 60973016, 61272175, and the National Basic Research 973 Program of China under Grant No. 2010CB328004.

布尔满足(SAT)是计算机科学、人工智能和运筹学研究中的著名问题。本文主要解决具有Model RB结构的满足性问题。Model RB与图着色等问题具有相似的结构。本文基于Model RB结构的特性提出了一种转换方式和三个高效的完全SAT求解算法。本文将问题子句转换成图的形式, 并用互斥集和关联集来表示变量之间的关系。通过使用图中的节点权值和团来确定搜索顺序的方式降低搜索深度。结果显示本文的算法在多数Model RB测试基准中比最好的SAT求解器具有更好的性能。特别是对那些大的测试基准, 本文的求解器具有更好的运行效率。

Abstract: Boolean satisfiability (SAT) is a well-known problem in computer science, artificial intelligence, and operations research. This paper focuses on the satisfiability problem ofModel RB structure that is similar to graph coloring problems and others. We propose a translation method and three effective complete SAT solving algorithms based on the characterization of Model RB structure. We translate clauses into a graph with exclusive sets and relative sets. In order to reduce search depth, we determine search order using vertex weights and clique in the graph. The results show that our algorithms are much more effective than the best SAT solvers in numerous Model RB benchmarks, especially in those large benchmark instances.

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