Empirical investigation of stochastic local search for maximum satisfiability View Full Text


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Article Info

DATE

2019-02

AUTHORS

Yi Chu, Chuan Luo, Shaowei Cai, Haihang You

ABSTRACT

The maximum satisfiability (MAX-SAT) problem is an important NP-hard problem in theory, and has a broad range of applications in practice. Stochastic local search (SLS) is becoming an increasingly popular method for solving MAX-SAT. Recently, a powerful SLS algorithm called CCLS shows efficiency on solving random and crafted MAX-SAT instances. However, the performance of CCLS on solving industrial MAX-SAT instances lags far behind. In this paper, we focus on experimentally analyzing the performance of SLS algorithms for solving industrial MAXSAT instances. First, we conduct experiments to analyze why CCLS performs poor on industrial instances. Then we propose a new strategy called additive BMS (Best from Multiple Selections) to ease the serious issue. By integrating CCLS and additive BMS, we develop a new SLS algorithm for MAXSAT called CCABMS, and related experiments indicate the efficiency of CCABMS. Also, we experimentally analyze the effectiveness of initialization methods on SLS algorithms for MAX-SAT, and combine an effective initialization method with CCABMS, resulting in an enhanced algorithm. Experimental results show that our enhanced algorithm performs better than its state-of-the-art SLS competitors on a large number of industrial MAX-SAT instances. More... »

PAGES

1-13

References to SciGraph publications

  • 2009. Exploiting Cycle Structures in Max-SAT in THEORY AND APPLICATIONS OF SATISFIABILITY TESTING - SAT 2009
  • 2013. Improving WPM2 for (Weighted) Partial MaxSAT in PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING
  • 2011. Sequential Model-Based Optimization for General Algorithm Configuration in LEARNING AND INTELLIGENT OPTIMIZATION
  • 2014-04. A hybrid multi-objective PSO algorithm with local search strategy for VLSI partitioning in FRONTIERS OF COMPUTER SCIENCE
  • 2001-09. Efficient 2 and 3-Flip Neighborhood Search Algorithms for the MAX SAT: Experimental Evaluation in JOURNAL OF HEURISTICS
  • 2016. Monte-Carlo Tree Search for the Maximum Satisfiability Problem in PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING
  • 2003-05-27. Iterated Robust Tabu Search for MAX-SAT in ADVANCES IN ARTIFICAL INTELLIGENCE
  • 2000-02. Guided Local Search for Solving SAT and Weighted MAX-SAT Problems in JOURNAL OF AUTOMATED REASONING
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    http://scigraph.springernature.com/pub.10.1007/s11704-018-7107-z

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    http://dx.doi.org/10.1007/s11704-018-7107-z

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