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Efficient 2 and 3-Flip Neighborhood Search Algorithms for the MAX SAT: Experimental Evaluation

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Abstract

For problems SAT and MAX SAT, local search algorithms are widely acknowledged as one of the most effective approaches. Most of the local search algorithms are based on the 1-flip neighborhood, which is the set of solutions obtainable by flipping the truth assignment of one variable. In this paper, we consider r-flip neighborhoods for r = 2, 3, and examine their effectiveness by computational experiments. In the accompanying paper, we proposed new implementations of these neighborhoods, and showed that the expected size of 2-flip neighborhood is O(n + m) and that of 3-flip neighborhood is O(m + t 2 n), compared to their original size O(n 2) andO(n 3), respectively, where n is the number of variables, m is the number of clauses and t is the maximum number of appearances of one variable. These are used in this paper under the framework of tabu search and other metaheuristic methods, and compared with other existing algorithms with 1-flip neighborhood. The results exhibit good prospects of larger neighborhoods.

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References

  • Cha, B., K. Iwama, Y. Kambayashi, and S. Miyazaki. (1997). “Local Search Algorithms for Partial MAXSAT.” Proceedings of the 14th National Conference on Artificial Intelligence and 9th Innovative Applications of Artificial Intelligence Conference. pp. 263–268.

  • Freeman, J.W. (1995). “Improvements to Propositional Satisfiability Search Algorithms.” Dissertation, University of Pennsylvania.

  • Fukunaga, A.S. (1997). “Variable-Selection Heuristics in Local Search for SAT.” Proceedings of the 14th National Conference on Artificial Intelligence and 9th Innovative Application of Artificial Intelligence Conference. pp. 275–280.

  • Garey, M.R. and D.S. Johnson. (1979). Computers and Intractability: A Guide to the Theory of NP-Completeness. New York: Freeman.

    Google Scholar 

  • Gu, J. (1992). “Efficient Local Search for Very Large-Scale Satisfiability Problems.” SIGART Bulletin 3, 8–12.

    Google Scholar 

  • Hansen, P. and B. Jaumard. (1990). “Algorithms for the Maximum Satisfiability Problem.” Computing 44, 279–303.

    Google Scholar 

  • Hooker, J.N. and C. Fedjki. (1990). “Branch-and-Cut Solution of Inference Problems in Propositional Logic.” Annals of Mathematics and Artificial Intelligence 1, 123–139.

    Google Scholar 

  • Karmarkar, N., M.G.C. Resende, and K.G. Ramakrishnan. (1991). “An Interior Point Algorithm to Solve Computationally Difficult Set Covering Problems.” Mathematical Programming 52, 597–618.

    Google Scholar 

  • Mannino, C. and A. Sassano. (1995). “Solving Hard Set Covering Problems.” Operations Research Letters 18, 1–5.

    Google Scholar 

  • Mazure, B., L. Saïs, and É. Grégoire. (1997). “Tabu Search for SAT.” Proceedings of the 14th National Conference on Artificial Intelligence and 9th Innovative Applications of Artificial Intelligence Conference. pp. 281–285.

  • Nonobe, K. and T. Ibaraki. (1998). “A Tabu Search Approach to the CSP (Constraint Satisfaction Problem) as a General Problem Solver.” European Journal of Operational Research 106, 599–623.

    Google Scholar 

  • Odijk, M.A. and H. vanMaaren. (1998). “Improved Solutions to the Steiner Triple Covering Problem.” Information Processing Letters 65, 67–69.

    Google Scholar 

  • Pardalos, P.M., L.S. Pitsoulis, and M.G.C. Resende. (1996). “AParallel GRASP for MAX-SAT Problems.” Lecture Notes in Computer Science 1180, 575–585.

    Google Scholar 

  • Resende, M.G.C. and T.A. Feo. (1996). “A GRASP for Satisfiability.” In D.S. Johnson and M.A. Trick, (eds.), Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Vol. 26 of DIMACS Series on Discrete Mathematics and Theoretical Computer Science (American Mathematical Society, 1996) pp. 499–520.

  • Resende, M.G.C., L.S. Pitsoulis, and P.M. Pardalos. (1997). “Approximate Solution of Weighted MAX-SAT Problems Using GRASP.” In D.-Z. Du, J. Gu and P.M. Pardalos, (eds.), Satisfiability Problem: Theory and Applications, Vol. 35 of DIMACS Series on Discrete Mathematics and Theoretical Computer Science (American Mathematical Society, 1997) pp. 393–405.

  • Resende, M.G.C., L.S. Pitsoulis, and P.M. Pardalos. (2000). “FORTRAN Subroutines for Computing Approximate Solutions of Weighted MAX-SAT Problems Using GRASP.” Discrete Applied Mathematics 100, 95–113.

    Google Scholar 

  • Selman, B., H. Levesque, and D. Mitchell. (1992). “A New Method for Solving Hard Satisfiability Problems.” Proceedings of the 10th National Conference on Artificial Intelligence. pp. 440–446.

  • Selman, B. and H.A. Kautz. (1993a). “An Empirical Study of Greedy Local Search for Satisfiability Testing.” Proceedings of the 11th National Conference on Artificial Intelligence. pp. 46–51.

  • Selman, B. and H.A. Kautz. (1993b). “Domain-Independent Extensions toGSAT: Solving Large Structured Satisfi-ability Problems.” Proceedings of the 13th International Joint Conference on Artificial Intelligence. pp. 290–295.

  • Selman, B., H.A. Kautz, and B. Cohen. (1994). “Noise Strategies for Improving Local Search.” Proceedings of the 12th National Conference on Artificial Intelligence. pp. 337–343.

  • Selman, B., H.A. Kautz, and B. Cohen. (1996). “Local Search Strategies for Satisfiability Testing.” In D.S. Johnson and M.A. Trick (eds.), Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Vol. 26 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science (American Mathematical Society, 1996) pp. 521–531.

  • Yagiura, M. and T. Ibaraki. (1999). “Analyses on the 2 and 3-Flip Neighborhoods for the MAX SAT.” Journal of Combinatorial Optimization 3, 95–114.

    Google Scholar 

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Yagiura, M., Ibaraki, T. Efficient 2 and 3-Flip Neighborhood Search Algorithms for the MAX SAT: Experimental Evaluation. Journal of Heuristics 7, 423–442 (2001). https://doi.org/10.1023/A:1011306011437

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