Journal of Automated Reasoning

, Volume 24, Issue 4, pp 421–481

Local Search Algorithms for SAT: An Empirical Evaluation

  • Holger H. Hoos
  • Thomas Stützle

DOI: 10.1023/A:1006350622830

Cite this article as:
Hoos, H.H. & Stützle, T. Journal of Automated Reasoning (2000) 24: 421. doi:10.1023/A:1006350622830


Local search algorithms are among the standard methods for solving hard combinatorial problems from various areas of artificial intelligence and operations research. For SAT, some of the most successful and powerful algorithms are based on stochastic local search, and in the past 10 years a large number of such algorithms have been proposed and investigated. In this article, we focus on two particularly well-known families of local search algorithms for SAT, the GSAT and WalkSAT architectures. We present a detailed comparative analysis of these algorithms" performance using a benchmark set that contains instances from randomized distributions as well as SAT-encoded problems from various domains. We also investigate the robustness of the observed performance characteristics as algorithm-dependent and problem-dependent parameters are changed. Our empirical analysis gives a very detailed picture of the algorithms" performance for various domains of SAT problems; it also reveals a fundamental weakness in some of the best-performing algorithms and shows how this can be overcome.

SATstochastic searchempirical evaluationrun-time distributionsrobustness

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • Holger H. Hoos
  • Thomas Stützle

There are no affiliations available