Solving Sequencing Problems Using Recurrent Neural Networks and Simulated Annealing - A Structural and Computational Comparison -

  • Kirti Singh
  • Bernd Schneider
  • Karl Kurbel
Conference paper
Part of the Operations Research Proceedings book series (ORP, volume 1994)

Summary

Since most sequencing problems are NP-hard, many heuristic techniques have been tried to solve such problems. In this paper, we investigate two methods - Hopfield neural networks and Simulated Annealing - and their application to the problem of placing standard cells during VLSI design. Comparisons were made with respect to solution quality and computing time. Simulated Annealing performed better in all test cases, but the differences between solution qualities decrease as problem sizes and complexities are higher. Advantages and disadvantages of both methods, and approaches to improve their performance are discussed.

Keywords

Crystallization Bala 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Hopfield, J.J., Tank, D.W.: Neural Computation of Decisions in Optimization Problems; in: Biological Cybernetics 52 (1985) 2, pp. 147–152.Google Scholar
  2. [2]
    Singh, K., George, S.M., Rambabu, P.: Recurrent Networks for Standard Cell Placement; in: Balagurusamy, E., Sushila, B. (eds.): Artificial Intelligence Technology - Applications and Management; New Delhi, New York et al. 1993, pp. 141–148.Google Scholar
  3. [3]
    Kuck, N., Middendorf, M., Schmeck, H.: Generic Branch-and-Bound on a Network of Transputers; Bericht 283, Institut für Angewandte Informatik und Formale Beschreibungsverfahren, Universität Karlsruhe, November 1993.Google Scholar
  4. [4]
    Starkweather, T., Whitley, D., Mathias, K., McDaniel, S.: Sequence Scheduling with Genetic Algorithms; in: Fandel, G., Gulledge, Th., Jones, A. (eds.): New Directions for Operations Research in Manufacturing; Berlin et al. 1991, pp. 129–148.Google Scholar
  5. [5]
    Domschke, W., Forst, P., Voß, S.: Tabu Search Techniques for the Quadratic Semi-Assignment Problem; in: Fandel, G., Gulledge, Th., Jones, A. (eds.): New Directions for Operations Research in Manufacturing; Berlin et al. 1991, pp. 389–405.Google Scholar
  6. [6]
    Dorn, J., Froeschl, K.A.: Scheduling of Production Processes; New York et al. 1993.Google Scholar
  7. [7]
    Aarts, E.H.L., Korst, J.H.M.: Simulated Annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing; New York 1989.Google Scholar
  8. [8]
    Kurbel, K.E.: Production Scheduling in a Leitstand System Using a Neural-net Approach; in: Balagurusamy, E., Sushila, B. (eds.): Artificial Intelligence Technology - Applications and Management; New Delhi, New York et al. 1993, pp. 297–305.Google Scholar
  9. [9]
    Shahookar, K., Mazumder, P.: VLSI Placement Techniques; ACM Computing Surveys 23 (1991) 2, pp. 143–220.CrossRefGoogle Scholar
  10. [10]
    Kirkpatrick, S., Gelatt (Jr.), C. D., Vecchi, M. P.: Optimization by Simulated Annealing; Science 220(1983) 5, pp. 571–680.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Kirti Singh
    • 1
  • Bernd Schneider
    • 2
  • Karl Kurbel
    • 2
  1. 1.IndoreIndia
  2. 2.MünsterGermany

Personalised recommendations