Trap escape for local search by backtracking and conflict reverse
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This paper presents an efficient trap escape strategy in stochastic local search for Satisfiability. The proposed method aims to enhance local search by pro- viding an alternative local minima escaping strategy. Our variable selection scheme provides a novel local minima escaping mechanism to explore new solution areas. Conflict variables are hypothesized as variables recently selected near local min- ima. Hence, a list of backtracked conflict variables is retrieved from local min- ima. The new strategy selects variables in the backtracked variable list based on the clause-weight scoring function and stagnation weights and variable weights as tiebreak criteria. This method is an alternative to the conventional method of se- lecting variables in a randomized unsatisfied clause. The proposed tiebreak method favors high stagnation weights and low variable weights during trap escape phases. The new strategies are examined on verification benchmark and SAT Competi- tion 2011 and 2012 application and crafted instances. Our experiments show that proposed strategy has comparable performance with state-of-the-art local search solvers for SAT.
Twelfth Scandinavian Conference on Artificial Intelligence
Copyright 2013 IOS Press. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the publisher website for access to the definitive, published version.
Artificial Intelligence and Image Processing not elsewhere classified