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Application of Ant Colony Optimization Algorithm to Multi-Join Query Optimization

  • Nana Li
  • Yujuan Liu
  • Yongfeng Dong
  • Junhua Gu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5370)

Abstract

Multi-join query optimization (MJQO) is an important technique for designing and implementing database manage system. It is a crucial factor that affects the capability of database. This paper proposes a new algorithm to solve the problem of MJQO based on ant colony optimization (ACO). In this paper, details of the algorithm used to solve MJQO problem have been interpreted, including how to define heuristic information, how to implement local pheromone update and global pheromone update and how to design state transition rule. After repeated iteration, a reasonable solution is obtained. Compared with genetic algorithm, the simulation result indicates that ACO is more effective and efficient.

Keywords

Multi-join query optimization ant colony optimization algorithm (ACO) pheromone heuristic information 

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References

  1. 1.
    Shekita, E., Young, H., Tan, K.L.: Multi-join optimization for symmetric multiprocessors. In: Proc. Of the Conf. on Very Large Data Bases (VLDB), Dublin, Ireland, pp. 479–492 (1993)Google Scholar
  2. 2.
    Krishnamurthy, W.R., Boral, H., Zaniolo, C.: Optimization of nonrecursive queries. In: Proc. Of the Conf. On Very Large Data Base (sVLDB), Kyoto, Japan, pp. 128–137 (1986)Google Scholar
  3. 3.
    Cao, Y., Fang, Q.: Parallel Query Optimization Techniques for Multi-Join Expressions Based on Genetic Algorithmspline. Journal of Software 13(2), 250–256 (2002)Google Scholar
  4. 4.
    Swami, A., Iyer, B.: A polynomial time algorithm for optimizing join queries. In: Proc. IEEE Conf. on Data Engineering, Vienna, Austria, pp. 345–354 (1993)Google Scholar
  5. 5.
    Dorigom, M., Gambardella, L.M.: Ant colonies for the traveling salesman problem. BioSystems 43(2), 73–81 (1997)CrossRefGoogle Scholar
  6. 6.
    Maniezzo, V., Dorigo, M., Colorni, A.: The ant system applied to the quadratic assignmentproblem, IRIDIA/94-28. Universite de Bruxelles, Belgium (1994)Google Scholar
  7. 7.
    Colorni, A., Dorigo, M., Maniezzo, V., et al.: Ant system for job-shop scheduling. Belgian Journal of Operations Research, Statistics and Computer Science 4(1), 39–53 (1994)MATHGoogle Scholar
  8. 8.
    Bullnheimer, B., Hartl, R.F., Strauss, C.: Applying the ant system to the vehicle routing problem. In: Osman, I.H., Vo, S., Martello, S., Roucairol, C. (eds.) Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pp. 109–120. Kluwer Academics, Dordrecht (1998)Google Scholar
  9. 9.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by a Colony of Cooperating Agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B 26(1), 29–41 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Nana Li
    • 1
  • Yujuan Liu
    • 1
  • Yongfeng Dong
    • 1
  • Junhua Gu
    • 1
  1. 1.College of Computer Science and SoftwareHebei University of TechnologyTianjinChina

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