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The Journal of Supercomputing

, Volume 26, Issue 3, pp 221–238 | Cite as

On Enforced Convergence of ACO and its Implementation on the Reconfigurable Mesh Architecture Using Size Reduction Tasks

  • Stefan Janson
  • Daniel Merkle
  • Martin Middendorf*
  • Hossam Elgindy
  • Hartmut Schmeck
Article

Abstract

In this paper we show that size reduction tasks can be used for executing iterative randomized metaheuristics on runtime reconfigurable architectures so that an improved throughput and better solution qualities are obtained compared to conventional architectures that do not allow runtime reconfiguration. In particular, the problem of executing ant colony optimization (ACO) algorithms on a dynamically reconfigurable mesh architecture is studied. It is shown how ACO can be implemented such that the convergence behavior of the algorithm can be used to dynamically reduce the size of the submesh that is needed for execution. Furthermore we propose a method to enforce the convergence of ACO leading to a faster reduction process. This increases the throughput of ACO algorithms on runtime reconfigurable meshes. The increased throughput is used for repeated runs of ACO algorithms on a given set of problem instances which significantly improves the obtained solution quality.

scheduling ant colony optimization run-time reconfigurability reconfigurable mesh 

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References

  1. 1.
    A. Bauer, B. Bullnheimer, R. F. Hartl, and C. Strauss. An ant colony optimization approach for the single machine total tardiness problem. In Proceedings of the 1999 Congress on Evolutionary Computation (CEC99), pp. 1445–1450. Washington D.C., 1999.Google Scholar
  2. 2.
    Y. Ben-Asher, K.-J. Lange, D. Peleg, and A. Schuster. The complexity of reconfiguring network models. Information and Computation, 121:41–58, 1995.Google Scholar
  3. 3.
    M. L den Besten, T. Stützle, and M. Dorigo. Ant colony optimization for the total weighted tardiness problem. In M. Schoenauer et al., eds., Parallel Problem Solving from Nature: 6th Int. Conf., vol. 1917 pp. 611–620. Springer, Berlin, LNCS, 2000.Google Scholar
  4. 4.
    V. Bokka, K. Nakano, S. Olariu, J. L. Schwing, and L. Wilson. Optimal algorithms for the multiple query problem on reconfigurable meshes, with applications. IEEE Transactions on Parallel and Distributed Systems, 12:875–887, 2001.Google Scholar
  5. 5.
    K. Bondalapati and V. K. Prasanna. Reconfigurable meshes: Theory and practice. In R. W. Hartenstein and Viktor K. Prasanna, eds., Proceedings Reconfigurable Architectures Workshop, Geneva, Switzerland. Bruchsal, ITpress Verlag.Google Scholar
  6. 6.
    R. E. Burkard, S. E. Karisch, and F. Rendl. QAPLIB-A quadratic assignment problem library. Journal of Global Optimization, 10:391–403, 1997. QAPLIB: http://www.opt.math.tu-graz.ac.at/qaplib/Google Scholar
  7. 7.
    O. Diessel, H. ElGindy, M. Middendorf, B. Schmidt, and H. Schmeck. Dynamic scheduling of tasks on partially reconfigurable FPGAs. IEE-Proceedings-Computer and Digital Techniques, 147:181–188, 2000.Google Scholar
  8. 8.
    M. Dorigo and G. Di Caro. The ant colony optimization meta-heuristic. In D. Corne, M. Dorigo, and F. Glover, eds., New Ideas in Optimization, pp. 11–32. McGraw-Hill, 1999.Google Scholar
  9. 9.
    M. Dorigo and L. M. Gambardella. Ant colonies for the QAP. Technical Report IDSIA-4-97, IDSIA, Lugano, 1997.Google Scholar
  10. 10.
    M. Dorigo and L. M. Gambardella. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput., 1:53–66, 1997.Google Scholar
  11. 11.
    B. Echermann and H. J. Wunderlich. Optimized synthesis of self-testable finite state machines. In 20th Int. Symp. on Fault-Tolerant Computing (FFTCS 20), Newcastle upon Tyne, 1990.Google Scholar
  12. 12.
    L. M. Gambardella, E. Taillard, and M. Dorigo. Ant colonies for the quadratic assignment problem. Journal of the Operational Research Society, 50:167–176, 1999.Google Scholar
  13. 13.
    J.-W. Jang, M. Nigam, V. K. Prasanna, and S. Sahni. Constant time algorithms for computational geometry on the reconfigurable mesh. IEEE Transactions on Parallel and Distributed Systems, 8:1–12, 1997.Google Scholar
  14. 14.
    V. Maniezzo. Exact and approximate nondeterministic tree-search procedures for the quadratic assignment problem. INFORMS Journal on Computing, 11:358–368, 1999.Google Scholar
  15. 15.
    V. Maniezzo and A. Colorni. The ant system applied to the quadratic assignment problem. IEEE Transactions on Knowledge and Data Engineering, 11:769–778, 1999.Google Scholar
  16. 16.
    V. Maniezzo, A. Colorni, and M. Dorigo. The Ant System applied to the quadratic assignment problem. Tech. Rep. IRIDIA/94-28, Universit Libre de Bruxelles, Belgium, 1994.Google Scholar
  17. 17.
    P. Martin. A hardware implementation of a genetic programming system using FPGAs and Handel-C. Genetic Programming and Evolvable Machines, 2:317–343, 2001.Google Scholar
  18. 18.
    G. M. Megson and I. M. Bland. A generic systolic array for genetic algorithms. IEEE Proceedings on Computers and Digital Techniques, 144:107–121, 1997.Google Scholar
  19. 19.
    D. Merkle and M. Middendorf. An ant algorithm with global pheromone evaluation for scheduling a single machine, to appear in Applied Intelligence.Google Scholar
  20. 20.
    D. Merkle and M. Middendorf. Fast ant colony optimization on runtime reconfigurable processor arrays, to appear in Genetic Programming and Evolvable Machines, 3(4), 2002.Google Scholar
  21. 21.
    D. Merkle, M. Middendorf, and H. Schmeck. Ant colony optimization for resource-constrained project scheduling. IEEE Transactions on Evolutionary Computation, 6:333–346, 2002.Google Scholar
  22. 22.
    M. Middendorf, H. Schmeck, H. Schröder, and G. Turner. Multiplication of matrices with different sparseness properties on dynamically reconfigurable meshes. VLSI Design, 9:69–81, 1999.Google Scholar
  23. 23.
    M. Middendorf, F. Reischle, and H. Schmeck. Multi colony ant algorithms. Journal of Heuristics, 8:305–320, 2002.Google Scholar
  24. 24.
    R. Miller, V. K. Prasanna-Kumar, D. I. Reisis, and Q. F. Stout. Parallel computations on reconfigurable meshes. IEEE Trans. Comput., 42:678–692, 1993.Google Scholar
  25. 25.
    K. Nakano and K. Wada. Integer summing algorithms on reconfigurable meshes. Theoretical Computer Science, 197:57–77, 1998.Google Scholar
  26. 26.
    T. Stützle. An ant approach for the flow shop problem. In Proc. 6th European Congress on Intelligent Techniques & Soft Computing (EUFIT '98), vol. 3, pp. 1560–1564. Verlag Mainz, Aachen, 1998.Google Scholar
  27. 27.
    T. Stützle and M. Dorigo. ACO algorithms for the quadratic assignment problem. In D. Corne, M. Dorigo, and F. Glover, eds., New Ideas in Optimization, pp. 33–50. McGraw-Hill, 1999.Google Scholar
  28. 28.
    T. Stützle and H. H. Hoos. The MAX-MIN ant system. Future Generation Computer Systems, 16:889–914, 2000.Google Scholar
  29. 29.
    J. Teich, S. P. Fekete, and J. Schepers. Optimization of dynamic hardware reconfigurations. The Journal of Supercomputing, 19:57–75, 2001.Google Scholar
  30. 30.
    H. Walder and M. Platzner. Non-preemptive multitasking on FPGAs: Task placement and footprint transform. In Proceedings of the 2nd International Conference on Engineering of Reconfigurable Systems and Algorithms (ERSA '02), pp. 24–30. CSREA Press, Las Vegas, Nevada, USA, 2002.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Stefan Janson
    • 1
  • Daniel Merkle
    • 1
  • Martin Middendorf*
    • 1
  • Hossam Elgindy
    • 2
  • Hartmut Schmeck
    • 3
  1. 1.Parallel Computing and Complex Systems GroupUniversity of LeipzigLeipzigGermany
  2. 2.School of Computer Science and EngineeringThe University of New South WalesSydneyAustralia
  3. 3.AIFB InstituteUniversity of KarlsruheKarlsruheGermany

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