A multistage optimization method based on WALKSAT and clustering for the hard MAX-SAT problems
Chinese Control Conference, CCC(2012)
摘要
It is widely recognized that WALKSAT is the one of the most effective local search algorithm for the satisfiability (SAT) and maximum satisfiability (MAX-SAT) problems. Inspired by the idea of population learning the large-scale structure of the landscape, this paper presents a multistage optimization method called MS-WALKSAT, which is based on WALKSAT and clustering. The experimental results on a variety of large and hard MAX-SAT problem instances have shown the MS-WALKSAT provides better performance than most of the reported algorithms. © 2012 Chinese Assoc of Automati.
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关键词
clustering,maximum satisfiability problems,multistage optimization,walksat,sociology,local search algorithm,clustering algorithms,physics,learning artificial intelligence,computability,noise
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