Skip to main content
SpringerLink
Log in

ISSATA: An algorithm for solving the 3-satisfiability problem based on improved strategy

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Stochastic local search algorithm with configuration check strategy can effectively solve random satisfiability instances, so configuration check strategy is widely used in combinatorial optimization problems. Inspired by this, we proposed an ISSATA algorithm to solve the 3-satisfiability problem. In this algorithm, a new initialization strategy is given, which can assign initial values to variables more efficiently. At the same time, a new variable selection strategy and a new neighbor priority strategy are proposed to improve the performance of selecting flipped variables. Comparative experiments conducted in public datasets show that ISSATA has better solution accuracy and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Cook SA (1971) The complexity of theorem-proving procedures. In: Proceedings of the third annual ACM symposium on Theory of computing (STOC ’71). ACM, pp 151–158

  2. Luo C, Cai S, Wu W, Jie Z, Su K (2015) CCLS: An efficient local search algorithm for weighted maximum satisfiability. IEEE Trans Comput 64(7):1830–1843

    Article  MathSciNet  Google Scholar 

  3. Biere A, Cimatti A, Clarke EM, Fujita M, Zhu Y (1999) Symbolic model checking using sat procedures instead of BDDs. In: Proceedings of the 36th conference on design automation, New Orleans, LA, USA, pp 317–320

  4. Hung WNN, Narasimhan N (2004) Reference model based RTL verification: an integrated approach. In: Proceeding of the ninth IEEE international high-level design validation and test workshop, Sonoma Valley, CA, USA, pp 9–13

  5. Wood R, Rutenbar R (1997) FPGA Routing and RoutabilityEstimation via Boolean Satisfiability. In: Proceedings of the 1997 ACM/SIGDA fifth international symposiumon field programmable gate arrays, FPGA, Monterey, CA, USA, pp 119–125

  6. Davis M, Putnam H (1960) A computing procedure for quantification theory. J ACM 7 (3):201–215

    Article  MathSciNet  Google Scholar 

  7. Silva JPM, Lynce I, Malik S (2009) Conflict-driven clause learning SAT solvers. In: Biere A, Heule M , Maaren H, Walsh T (eds) Handbook of satisfiability. Frontiers in artificial intelligence and applications, vol 185, pp 131–153

  8. Gomes CP, Kautz H, Sabharwal A, Selman B (2008) Chapter 2 satisfiability solvers. In: Harmelen F, Lifschitz V, Porter B (eds) Handbook of knowledge representation. Foundations of artificial intelligence, vol 3, pp 89–134

  9. Skvortsov ES (2009) A theoretical analysis of search in GSAT. In: Oliver K (ed) Theory and applications of satisfiability testing - SAT 2009. Lecture notes in computer science, vol 5584, pp 265–275

  10. Li CM, Li Y (2012) Satisfying versus falsifying in local search for satisfiability. In: Alessandro C, Roberto S (eds) Theory and applications of satisfiability testing - SAT 2012. Lecture notes in computer science, vol 7317, pp 477–478

  11. Gableske O, Heule MJH (2011) EagleUP: Solving random 3-SAT using SLS with unit propagation. In: Sakallah KA, Simon L (eds) Theory and applications of satisfiability testing - SAT 2011. Lecture notes in computer science, vol 6695, pp 367–368

  12. Zhang Y, Li B, Meng Q, Hu Q, Ma Q (2015) The experimental analysis of the efficiency of genetic algorithm based on 3-satisfiability problem. In: 2015 11th International conference on natural computation (ICNC), Zhangjiajie, China, pp 245–249

  13. Raschip M, Croitoru C, Frasinaru C (2018) New evolutionary approaches for SAT solving. In: Proceedings of the IEEE 30th international conference on tools with artificial intelligence (ICTAI), Volos, Greece, pp 522–526

  14. Guo Y, Zhang C (2017) A hybrid artificial bee colony algorithm for satisfiability problems based on tabu search. In: Proceedings of the 3rd IEEE international conference on computer and communications (ICCC), Chengdu, China, pp 2226–2230

  15. Guo Y, Zhang C (2017) Research on neighborhood search strategy of artificial bee colony algorithm for satisfiability problems. In: Proceedings of the 10th international symposium on computational intelligence and design (ISCID), Hangzhou, China, pp 123–126

  16. Youness H, Osama M, Hussein A, Moness M, Hassan AM (2020) An effective sat solver utilizing ACO based on heterogenous systems. IEEE Access 8:102920–102934

    Article  Google Scholar 

  17. Hossen MS, Polash MMA (2019) Implementing an efficient SAT solver for structuredy instances. In: Proceedings of the 8th international conference on informatics, electronics & vision (ICIEV) and the 3rd international conference on imaging, vision & pattern recognition (icIVPR), Spokane, USA, pp 238–242

  18. Hoos HH, Stützle T (2005) SLS METHODS. In: Hoos HH, Stützle T (eds) Stochastic local search. The Morgan Kaufmann series in artificial intelligence, pp 61–112

  19. Gao J, Li R, Yin M (2017) A randomized diversification strategy for solving satisfiability problem with long clauses. Sci China Inf Sci 60:092109

    Article  Google Scholar 

  20. Szeider S (2011) The parameterized complexity of k-flip local search for SAT and MAX SAT. Discret Optim 8(1):139–145

    Article  MathSciNet  Google Scholar 

  21. Balint A, Schöning U (2012) Choosing Probability Distributions for Stochastic Local Search and the Role of Make versus Break. In: Alessandro C, Roberto S (eds) Theory and applications of satisfiability testing - SAT 2012. Lecture notes in computer science, vol 7317, pp 16–29

  22. Tompkins DAD, Balint A, Hoos HH (2011) Captain Jack: New variable selection heuristics in local search for SAT. In: TSakallah KA, Simon L (eds) Theory and applications of satisfiability testing - SAT 2011. Lecture notes in computer science, vol 6695, pp 302–316

  23. Balint A, Schöning U (2016) Engineering a lightweight and efficient local search SAT Solver. In: Kliemann L, Sanders P (eds) Algorithm engineering. Lecture notes in computer science, vol 9220, pp 1–18

  24. Hutter F, Tompkins DAD, Hoos HH (2002) Scaling and probabilistic smoothing: efficient dynamic local search for SAT. In: Proceedings of the 8th international conference on the principles and practice of constraint Programming, Berlin, Germany, pp 233– 248

  25. Thornton J, Pham DN, Bain S, Ferreira V (2004) Additive versus multiplicative clause weighting for SAT. In: Proceedings of the 19th national conference on artificial intelligence, San Jose, USA, pp 191–196

  26. Michiels W, Aarts EHL, Korst JHM (2007) Metaheuristics. Theoretical aspects of local search. Springer, Berlin Heidelberg, pp 135–147

    MATH  Google Scholar 

  27. Smyth K, Hoos HH, Stützle T (2003) Iterated robust tabu search for MAX-SAT. In: Xiang Y, Chaib-draa B (eds) Advances in artificial intelligence. Lecture notes in computer science (Lecture notes in artificial intelligence), vol 2671, pp 129–144

  28. Gaspero LD, Schaerf A (2007) A composite-neighborhood tabu search approach to the traveling tournament problem. J Heuristics 13(2):189–207

    Article  Google Scholar 

  29. Abramé A, Habet D, Toumi D (2017) Improving configuration checking for satisfiable random k-SAT instances. Ann Math Artif Intell 79:5–24

    Article  MathSciNet  Google Scholar 

  30. Cai S, Su K (2013) Local search for Boolean Satisfiability with configuration checking and subscore. Artif Intell 204:75–98

    Article  Google Scholar 

  31. Luo C, Cai S, Su K, Wu W (2015) Clause states based configuration checking in local search for satisfiability. IEEE Trans Cybern 45(5):1028–1041

    Article  Google Scholar 

  32. Peng Cong, Zhongwei X u, Mei Meng (2020) Applying aspiration in local search for satisfiability. PLOS ONE Public Libr Sci 15(4):1–16

    Google Scholar 

  33. Mertens S, Mezard M, Zecchina R (2006) Threshold values of random k-SAT from the cavity method. Random Struct Algorithm 28(3):340–373

    Article  MathSciNet  Google Scholar 

  34. Duong TT, Pham DN, Sattar A, Newton MA (2013) Weight-enhanced diversification in stochastic local search for satisfiability. In: Proceedings of the 23rd international joint conference on artificial intelligence, Beijing, China, pp 524–530

  35. Cai S, Su K, Sattar A (2011) Local search with edge weighting and configuration checking heuristics for minimum vertex cover. Artif Intell 175(9-10):1672–1696

    Article  MathSciNet  Google Scholar 

  36. Cai S, Lin J, Luo C (2017) Finding a small vertex cover in massive sparse graphs: Construct, local search, and preprocess. J Artif Intell Res 59:463–494

    Article  MathSciNet  Google Scholar 

  37. AlKasem HH, Menai MEB (2020) Stochastic local search for Partial Max-SAT: an experimental evaluation. Artif Intell Rev 2:1– 42

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Computer Science, Chongqing University, 400044, Chongqing, China

    Ping Guo & Yang Zhang

  2. Chongqing Key Laboratory of Software Theory and Technology, 400044, Chongqing, China

    Ping Guo

Authors
  1. Ping Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ping Guo.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, P., Zhang, Y. ISSATA: An algorithm for solving the 3-satisfiability problem based on improved strategy. Appl Intell 52, 1740–1751 (2022). https://doi.org/10.1007/s10489-021-02493-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02493-1

Keywords

Search

Navigation