CCEHC: An efficient local search algorithm for weighted partial maximum satisfiability

doi:10.1016/j.artint.2016.11.001

	CCEHC: An efficient local search algorithm for weighted partial maximum satisfiability
	Luo, Chuan 1,2; Cai, Shaowei 3; Su, Kaile 4,5; Huang, Wenxuan 6
	2017-02-01
发表期刊	ARTIFICIAL INTELLIGENCE
ISSN	0004-3702
卷号	243 页码:26-44
摘要	Weighted maximum satisfiability and (unweighted) partial maximum satisfiability (PMS) are two significant generalizations of maximum satisfiability (MAX-SAT), and weighted partial maximum satisfiability (WPMS) is the combination of the two, with more important applications in practice. Recently, great breakthroughs have been made on stochastic local search (SLS) for weighted MAX-SAT and PMS, resulting in several state-of-the-art SLS algorithms CCLS, Dist and DistUP. However, compared to the great progress of SLS on weighted MAX-SAT and PMS, the performance of SLS on WPMS lags far behind. In this paper, we present a new SLS algorithm named CCEHC for WPMS. CCEHC employs an extended framework of CCLS with a heuristic emphasizing hard clauses, called EHC. With strong accents on hard clauses, EHC has three components: a variable selection mechanism focusing on configuration checking based only on hard clauses, a weighting scheme for hard clauses, and a biased random walk component. Extensive experiments demonstrate that CCEHC significantly outperforms its state-of-the-art SLS competitors. Further experimental results on comparing CCEHC with a state-of-the-art complete solver show the effectiveness of CCEHC on a number of application WPMS instances, and indicate that CCEHC might be beneficial in practice. Also, empirical analyses confirm the effectiveness of each component underlying the EHC heuristic. (C) 2016 Elsevier B.V. All rights reserved.
关键词	Local search Weighted partial maximum satisfiability Emphasis on hard clauses
DOI	10.1016/j.artint.2016.11.001
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2016YFB0200803] ; National Key Research and Development Program of China[2016YFC1401700] ; Open Project Program of the State Key Laboratory of Mathematical Engineering and Advanced Computing[2016A06] ; National Natural Science Foundation of China[61502464] ; National Natural Science Foundation of China[61572234] ; National Natural Science Foundation of China[61472369] ; National Natural Science Foundation of China[61370072] ; Australian Research Council[DP150101618]
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000392038100002
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：56[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7675
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cai, Shaowei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.State Key Lab Math Engn & Adv Comp, Wuxi 214125, Peoples R China 3.Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing 100190, Peoples R China 4.Jinan Univ, Coll Informat Sci & Technol, Guangzhou 510632, Guangdong, Peoples R China 5.Griffith Univ, Inst Integrated & Intelligent Syst, Brisbane, Qld 4111, Australia 6.MIT, Dept Mat Sci & Engn, Cambridge, MA 02139 USA
推荐引用方式 GB/T 7714	Luo, Chuan,Cai, Shaowei,Su, Kaile,et al. CCEHC: An efficient local search algorithm for weighted partial maximum satisfiability[J]. ARTIFICIAL INTELLIGENCE,2017,243:26-44.
APA	Luo, Chuan,Cai, Shaowei,Su, Kaile,&Huang, Wenxuan.(2017).CCEHC: An efficient local search algorithm for weighted partial maximum satisfiability.ARTIFICIAL INTELLIGENCE,243,26-44.
MLA	Luo, Chuan,et al."CCEHC: An efficient local search algorithm for weighted partial maximum satisfiability".ARTIFICIAL INTELLIGENCE 243(2017):26-44.