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Received: 2010-09-09

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.7 P.589-596

http://doi.org/10.1631/jzus.C1000313


Modified extremal optimization for the hard maximum satisfiability problem


Author(s):  Guo-qiang Zeng, Yong-zai Lu, Wei-Jie Mao

Affiliation(s):  College of Physics & Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China, State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zeng.guoqiang5@gmail.com

Key Words:  Extremal optimization (EO), Evolution, Probability distributions, Maximum satisfiability (MAXSAT) problem


Guo-qiang Zeng, Yong-zai Lu, Wei-Jie Mao. Modified extremal optimization for the hard maximum satisfiability problem[J]. Journal of Zhejiang University Science C, 2011, 12(7): 589-596.

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Abstract: 
Based on our recent study on probability distributions for evolution in extremal optimization (EO), we propose a modified framework called EOSAT to approximate ground states of the hard maximum satisfiability (MAXSAT) problem, a generalized version of the satisfiability (SAT) problem. The basic idea behind EOSAT is to generalize the evolutionary probability distribution in the Bose-Einstein-EO (BE-EO) algorithm, competing with other popular algorithms such as simulated annealing and WALKSAT. Experimental results on the hard MAXSAT instances from SATLIB show that the modified algorithms are superior to the original BE-EO algorithm.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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