Journal of Computer Science and Technology

, Volume 17, Issue 3, pp 340–346 | Cite as

An algorithm based on tabu search for satisfiability problem

  • Huang Wenqi Email author
  • Zhang Defu 
  • Wang Houxiang 


In this paper, a computationally effective algorithm based on tabu search for solving the satisfiability problem (TSSAT) is proposed. Some novel and efficient heuristic strategies for generating candidate neighborhood of the current assignment and selecting variables to be flipped are presented. Especially, the aspiration criterion and tabu list structure of TSSAT are different from those of traditional tabu search. Computational experiments on a class of problem instances show that, TSSAT, in a reasonable amount of computer time, yields better results than Novelty which is currently among the fastest known. Therefore, TSSAT is feasible and effective.


satisfiability tabu search local search 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Li Wei, Huang Xiong. The analysis of algorithms for the proposition logic satisfiability.Computer Science, 1999, 26(3): 1–9.Google Scholar
  2. [2]
    Gent I P, Walsh T. The search for satisfaction., 1999.Google Scholar
  3. [3]
    Davis M, Putnam H. A computing procedure for quantification theory.Journal of the ACM, 1960, 7: 201–215.zbMATHCrossRefMathSciNetGoogle Scholar
  4. [4]
    Selman B, Levesque H, Mitchell D. A new method for solving hard satisfiability problems. InProceedings of the 10th National Conference on AI, American Association for Artificial Intelligence, 1992, pp. 440–446.Google Scholar
  5. [5]
    Gu J. Efficient local search for very large-scale satisfiability problems.SIGART Bulletin, 1992, 3(1): 8–12.CrossRefGoogle Scholar
  6. [6]
    Gent I P, Walsh T. An empirical analysis of search in GSAT.Journal of Artificial Intelligence Research, 1993, 1: 47–59.zbMATHGoogle Scholar
  7. [7]
    Li Wei, Huang Wenqi. A physic-mathematical method for solving conjunctive normal form satisfiability problem.Science in China (Series A), 1994, 24(11): 1208–1217.Google Scholar
  8. [8]
    Selman B, Kautz H, Cohen B. Noise strategies for improving local search. InProceedings of the 12th National Conference on AI American Association for Artificial, Intelligence, 1994, pp. 337–343.Google Scholar
  9. [9]
    Spears W M. Simulated annealing for hard satisfiability problems. In Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Johnson D S, Trick M A (eds.), 1996, pp. 533–558.DIMA CS Series in Discrete Mathematics and Theoretical Computer Science, Volume 26, American Mathematical Society.Google Scholar
  10. [10]
    Mazure B, Sais L, Gregoire E. Tabu search for SAT. InProceedings of 14th National Conference on Artificial Intelligence, American Association for Artificial Intelligence, AAAI Press/The MIT Press, 1997, pp. 281–285.Google Scholar
  11. [11]
    Huang Wenqi, Jin Renchao. The quasi-physical personification algorithm for solving SAT problem-Solar.Science in China (Series E), 1997, 27(2): 179–186.Google Scholar
  12. [12]
    McAllester D, Selman B, Kautz H. Evidence for invariants in local search. InProceedings of the 14th National Conference on AI American Association for Artificial Intelligence, 1997, pp. 321–327.Google Scholar
  13. [13]
    Glover F, Laguna M. Tabu Search. Kluwer Academic Publishers, Boston, 1997.zbMATHGoogle Scholar
  14. [14]
    Holger H Hoos, Thomas Stützle. Towards a characterisation of the behavior of stochastic local search algorithms for SAT.Artificial Intelligence, 1999, 112(1/2): 213–232.zbMATHCrossRefMathSciNetGoogle Scholar
  15. [15]
    Mitchell D, Selman B, Levesque H J. Hard and easy distributions of SAT problems. InProc. AAAI-92, San Jose, CA, 1992, pp. 459–465.Google Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2002

Authors and Affiliations

  1. 1.School of ComputerHuazhong University of Science and TechnologyWuhanP. R. China

Personalised recommendations