Adaptive memory-based local search for MAX-SAT

  • Authors:

  • Zhipeng Lü;Jin-Kao Hao

  • Affiliations:

  • School of Computer Science and Technology, Huazhong University of Science and Technology, 430074 Wuhan, PR China and LERIA, Université d'Angers, 2 Boulevard Lavoisier, 49045 Angers, France;LERIA, Université d'Angers, 2 Boulevard Lavoisier, 49045 Angers, France

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2012

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Abstract

Many real world problems, such as circuit designing and planning, can be encoded into the maximum satisfiability problem (MAX-SAT). To solve MAX-SAT, many effective local search heuristic algorithms have been reported in the literature. This paper aims to study how useful information could be gathered during the search history and used to enhance local search heuristic algorithms. For this purpose, we present an adaptive memory-based local search heuristic (denoted by AMLS) for solving MAX-SAT. The AMLS algorithm uses several memory structures to define new rules for selecting the next variable to flip at each step and additional adaptive mechanisms and diversification strategies. The effectiveness and efficiency of our AMLS algorithm is evaluated on a large range of random and structured MAX-SAT and SAT instances, many of which are derived from real world applications. The computational results show that AMLS competes favorably, in terms of several criteria, with four state-of-the-art SAT and MAX-SAT solvers AdaptNovelty+, AdaptG2WSAT"p, IRoTS and RoTS.