Browse > Article
http://dx.doi.org/10.3745/KIPSTA.2011.18A.3.115

A Backtracking Search Framework for Constraint Satisfaction Optimization Problems  

Sohn, Surg-Won (호서대학교 벤처전문대학원)
Publication Information
The KIPS Transactions:PartA / v.18A, no.3, 2011 , pp. 115-122 More about this Journal
Abstract
It is very hard to obtain a general algorithm for solution of all the constraint satisfaction optimization problems. However, if the whole problem is separated into subproblems by characteristics of decision variables, we can assume that an algorithm to obtain solutions of these subproblems is easier. Under the assumption, we propose a problem classifying rule which subdivide the whole problem, and develop backtracking algorithms fit for these subproblems. One of the methods of finding a quick solution is efficiently arrange for any order of the search tree nodes. We choose the cluster head positioning problem in wireless sensor networks in which static characteristics is dominant and interference minimization problem of RFID readers that has hybrid mixture of static and dynamic characteristics. For these problems, we develop optimal variable ordering algorithms, and compare with the conventional methods. As a result of classifying the problem into subproblems, we can realize a backtracking framework for systematic search. We also have shown that developed backtracking algorithms have good performance in their quality.
Keywords
Backtracking Search; Constraint Satisfaction Optimization; Variable Ordering; Problem Classifying Rule; Radio Resource Allocation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 W.B. Heinzelman, A. Chandrakasan, and H. Balakrishnan, ''An application-specific protocol architecture for wireless microsensor networks'', IEEE Transactions on Wireless Communications, 1(4), pp.660-670, 2002.   DOI   ScienceOn
2 S. Zhou, Z. Luo, E. Wong, C.J. Tan, and J. Luo, "Interconnected RFID Reader Collision Model and its Application in Reader Anti-collision," 2007 IEEE International Conference on RFID, pp.212-219, 2007.   DOI
3 김원태, 안광선, 이성준, "RFID 시스템에서 비트 변화 감지를 이용한 가변 슬롯 트리 기반 충돌 방지 알고리즘," 정보처리학회논문지A, 제16-A권, 제4호, pp.289-298, 2009. 8.   과학기술학회마을   DOI   ScienceOn
4 C. Bessiere and J.-C. Regin, "MAC and combined heuristics: Two reasons to forsake FC (and CBJ?) on hard problems," in CP series. Lecture Notes in Computer Science, E. C. Freuder, Ed., Vol.1118. Springer, pp.61-75, 1996.
5 F. Boussemart, F. Hemery, C. Lecoutre, and L. Sais, "Boosting systematic search by weighting constraints." In Proc. 16th European Conference on Artificial Intelligence-ECAI'04, pp.146-150, IOS, 2004.
6 R.J. Wallace, "Determining the principles underlying performance variation in csp heuristics," International Journal on Artificial Intelligence Tools, Vol.17 Issue5, pp.857-880, Oct., 2008,   DOI   ScienceOn
7 B. Hnich, T. Walsh, and B. M. Smith, "Dual modelling of permutation and injection problems," Journal of Artificial Intelligence Ressearch, Vol.21, pp.357-391, 2004.
8 D. Brelaz, "New methods to color the vertices of a graph," Communication of the ACM, Vol.22, No.4, pp.251-256, 1979.   DOI   ScienceOn
9 B. M. Smith, "The Brelaz heuristic and optimal static orderings." in CP series, Lecture Notes in Computer Science, J. Jaffar, Ed., Vol.1713. Springer, pp.405-418, 1999.   DOI
10 J. Patel, J.W. Chinneck, "Active-constraint variable ordering for faster feasibility of mixed integer linear programs," Mathematical Programming, Vol.110 Issue3, pp.445-474 Sep., 2007.   DOI
11 E. C. Freuder, "A sufficient condition for backtrack-free search," Journal of ACM, Vol.29, No.1, pp.24-32, 1982.   DOI   ScienceOn
12 H. Terashima-Marin, J.C. Ortiz-Bayliss, P. Ross, and M. Valenzuela-Rendon, "Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems," In proceedings of the 10th annual conference on genetic and evolutionary computation, pp.571-578, ACM, 2008.
13 I. P. Gent, E. MacIntyre, P. Prosser, B. M. Smith, and T. Walsh, "An empirical study of dynamic variable ordering heuristics for the constraint satisfaction problem," in Principles and Practice of Constraint Programming, pp.179-193, 1996.
14 S. W. Golomb and L. D. Baumert, "Backtrack programming," Journal of the ACM, Vol.12, No.4, pp.516-524, 1965.   DOI
15 R. M. Haralick and G. L. Elliott, "Increasing tree search efficiency for constraint satisfaction problems," Artificial Intelligence, Vol.14, No.3, pp.263-313, 1980.   DOI   ScienceOn
16 Z. Yujun, X. Jinyun, and S. Haihe, "A category theoretic approach to search algorithms: Towards a unified implementation for branch-and-bound and backtracking," 4th Int'l Conference on Computer Science & Education, pp.845-850, Jul., 2009.
17 M. Ercsey-Ravasz, T. Roska, and Z. Neda, "Cellular neural networks for NP-hard optimization," 11th Int'l Workshop on Cellular Neural Networks and Their Applications, pp.52-56, Jul., 2008.
18 P. Hell and J. Nesetril, "Colouring, constraint satisfaction, and complexity," Computer Science Review, Vol.2, Issue3, pp.143-163, Dec., 2008.   DOI   ScienceOn
19 F. Rossi, P. van Beek, and T. Walsh (Eds.), Handbook of Constraint Programming, Elsevier, 2006.
20 S. Prestwich, "Local search and backtracking vs non-systematic backtracking," AAAI 2001 Fall Symposium on Using Uncertainty within Computation, 2001.