Skip to main content
Log in

Tabu search techniques

A tutorial and an application to neural networks

  • Theoretical Papers
  • Published:
Operations-Research-Spektrum Aims and scope Submit manuscript

Summary

Tabu Search is a general heuristic procedure for global optimization. Based on simple ideas it has been extremely efficient in getting almost optimal solutions for many types of difficult combinatorial optimization problems.

The principles of Tabu Search are discribed and illustrations are given. An example of problem type where the use of Tabu Search has drastically cut down the computational effort is presented; it consists of the learning process of an associative memory represented by a neural network.

Zusammenfassung

Tabu Search ist eine heuristische Methode, die für globale Optimierung mit viel Erfolg in verschiedenen Umständen angewandt wurde.

Die Grundideen der Methode werden erklärt und mit Beispielen illustriert. Eine Anwendung an ein Lernprozess im Gebiet der Neuronen Netzwerke wird beschrieben.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Amaldi E (1987) Problèmes d'apprentissage dans les réseaux de neurones, Diploma Project. Swiss Federal Institute of Technology, Lausanne, December 1987

    Google Scholar 

  2. Burkard RE, Rendl F (1984) A thermodynamically motivated simulation procedure for combinatorial optimization problems. Eur J Oper Res 17:169–174

    Google Scholar 

  3. Chams M, Hertz A, de Werra D (1987) Some experiments with simulated annealing for coloring graphs. Eur J Oper Res 32:260–266

    Google Scholar 

  4. Faigle U, Schrader R (1988) On the convergence of stationary distributions in simulated annealing algorithms. Inf Process Lett 27:189–194

    Google Scholar 

  5. Glover F (1986) Future paths for Integer Programming and Links to Artificial Intelligence. Comput Oper Res 13:533–549

    Google Scholar 

  6. Glover F (1988) Tabu Search, CAAI Report 88-3. University of Colorado, Boulder

    Google Scholar 

  7. Hansen P, Jaumard B (1987) Algorithms for the Maximum Satisfiability Problem, RUTCOR Research Report 43-87. Rutgers University, New Brunswick, NJ

    Google Scholar 

  8. Hertz A, de Werra D The Tabu Search Metaheuristic: how we used it to appear in Annals of Mathematics and Artificial Intelligence

  9. Hertz A, de Werra D (1987) Using Tabu Search Techniques for Graph Coloring. Computing 39:345–351

    Google Scholar 

  10. Hopfield JJ (1982) Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proc Nat Acad Sci USA 79:2554–2558

    Google Scholar 

  11. Personnaz L, Guyon I, Dreyfus G (1986) Collective computational properties of neural networks: New learning mechanisms. Phys Rev A34:4217–4227

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Werra, D., Hertz, A. Tabu search techniques. OR Spektrum 11, 131–141 (1989). https://doi.org/10.1007/BF01720782

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01720782

Keywords

Navigation