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加权约束满足问题的改进RDS符号代数决策图求解算法 被引量:1

Improved RDS Symbol Algebraic Decision Diagram Algorithm for Weighted Constraint Satisfaction Problem
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摘要 加权约束满足问题(WCSP)是一类约束最优化问题.文中基于RDS思想,从减少RDS分解的子问题个数及提高各个子问题的求解效率入手,提出WCSP的改进RDS符号代数决策图(ADD)求解算法.通过改进最多约束变量的变量选择法,引入RDS变量引导原问题的子问题分解,进而减少RDS中分解的子问题个数.利用变量的后向度,进一步改进子问题的分解方法.为提高各个子问题的求解效率,利用桶消元算法并结合ADD操作消去子问题中的非RDS变量,进而减少子问题中的变量个数,提高深度优先分支界定法的下界.在大量随机生成的测试用例上的实验证明文中算法的优越性. Weighted constraint satisfaction problem (WCSP) is a kind of constraint optimization problem. Based on Russian doll search (RDS) algorithm, an improved RDS symbolic algebraic decision diagram (ADD) algorithm for WCSP is proposed to reduce the number of sub-problems and improve the efficiency of solving sub-problems of original RDS algorithm. Through improving the most constraining variable (MCV) method of variable selection, a concept of RDS variable is introduced and conducted for nested decomposition of the original problem. Then, the number of sub-problems is decreased. Furthermore, the nested decomposition method is improved by variable back-degree. To improve the efficiency of solving sub-problems, the bucket elimination algorithm combined with ADD operation is exploited to eliminate the non-RDS variables. And thus the number of variables in the sub-problem is decreased and the lower bound is improved. Experiments on a large number of random generated testing cases demonstrate the superiority of the proposed algorithm.
出处 《模式识别与人工智能》 EI CSCD 北大核心 2015年第12期1074-1083,共10页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金项目(No.61363030 61262030 61100025) 广西自然科学基金项目(No.2014GXNSFAA118354) 广西高等学校高水平创新团队及卓越学者计划资助
关键词 加权约束满足问题(WCSP) RUSSIAN Doll Search(RDS) 代数决策图(ADD) 符号算法 Weighted Constraint Satisfaction Problem (WCSP), Russian Doll Search (RDS),Algebraic Decision Diagram (ADD), Symbolic Algorithm
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参考文献18

  • 1Dechter R. Bucket Elimination: A Unifying Framework for Reaso- ning. Artificial Intelligence, 1999, 113(1/2): 41-85.
  • 2Schiex T, Fargier H, Verfaillie G. Valued Constraint Satisfaction Problems : Hard and Easy Problems//Proc of the 14th International Joint Conference on Artificial Intelligence. Queber, Canada, 1995 :631-637.
  • 3Larrosa J, Schiex T. In the Quest of the Best Form of Local Consis- tency for Weighted CSP// Proc of the 18th International Joint Con- ference on Artificial Intelligence. Acapulco, Mexico, 2003 : 239 - 244.
  • 4de Givry S, Heras F, Zytnicki M, et al. Existential Arc Consisten- cy: Getting Closer to Full Arc Consistency in Weighted CSPs /! Proc of the 19th International Joint Conference on Artificial Intelli- gence. Hyderabad, India, 2005, V: 84-89.
  • 5Cooper M C, de Givry S, Schiex T. Optimal Soft Arc Consistency // Proc of the 20th International Joint Conference on Artificial Inte- lligence. Hyderabad, India, 2007:68-73.
  • 6Cooper M C, de Givry S, S6nehez M, et al. Virtual Are Consistency. for Weighted CSP//Proc of the 23rd National Conference on Artifi- cial Intelligence. Chicago, USA, 2008, I: 253-258.
  • 7Lee J H M, Leung K L. Towards Efficient Consistency Enforcement for Global Constraints in Weighted Constraint Satisfaction// Proc of the 21st International Joint Conference on Artificial Intelligence. Pasadena, USA, 2009:559-565.
  • 8Lee J H M, Leung K L. A Stronger Consistency for Soft Global Con- straints in Weighted Constraint Satisfaction// Proc of the 24th Na- tional Conference on Artificial Intelligence. Atlanta, USA, 2010: 121-127.
  • 9Lee J H M, Leung K L, Shum Y W. Consistency Techniques for Po- lytime Linear Global Cost Functions in Weighted Constraint Satisfac- tion. Constraints, 2014, 19(3): 270-308.
  • 10Verfaillie G, Lematre M, Schiex T. Russian Doll Search for Sol- ving Constraint Optimization Problems//Proc of the 13th National Conference on Artificial Intelligence. Portland, USA, 1996, 1: 181-187.

二级参考文献14

  • 1贺仁杰,谭跃进.加权约束满足问题的改进深度优先搜索算法[J].系统工程学报,2004,19(5):512-516. 被引量:5
  • 2徐周波,古天龙,赵岭忠.网络最大流问题的一种新的符号ADD求解算法[J].通信学报,2005,26(2):1-8. 被引量:15
  • 3Bachar R I, Frohm E A, Gaona C M, et al, Algebraic Decision Di- agrams and Their Applications. Formal Methods in Systems Design, 1997,10(2/3):171-206.
  • 4Hachtel G D, Somenzi F. A Symbolic Algorithm for Maximum Flow in 0-1 Networks. Formal Methods in System Design, 1997,10(2/3):207-219.
  • 5Chatalic P, Simon L. Multi-Resolution on Compressed Sets of Clauses//Proc of the 12th International Conference on Tools with Artificial Intelligence. Vancouver, Canada, 2000:2-10.
  • 6Pan Guoqiang, Vardi M Y. Symbolic Techniques in Satisfiability Solving.Journal of Automated Reasoning, 2005,35(1/3):25-50.
  • 7Larrosa J, Meseguer P. Exploiting the Use of DAC in Max-CSP// Proc of the 2nd International Conference on Principle and Practice of Constraint Programming. Cambridge, USA,1996:308-322.
  • 8Wallace R J. Directed Are Consistency Preprocessing// Proe of the Workshop on Constraint Processing. Berlin, Germany: Springer-Verlag,1995:121-137.
  • 9Larrosa J, Schiex T. Solving Weighted CSP by Maintaining Arc-Consistency. Artificial Intelligence, 2004, 159 (1/2):1-26.
  • 10Dechter R. Bucket Elimination: A Unifying Framework for Reason-ing. Artificial Intelligence,1999,113(1/2):41-85.

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