Not Necessary Improving Heuristics

  • Saïd Salhi


In this chapter, I discuss those popular heuristics that improve the solutions by not necessarily restricting the next move to be an improving move. Note that allowing such inferior solutions to be chosen need to be controlled as the search may diverge to even worse solutions. Some of the well-established techniques that are based on this concept are covered here. I concentrate on simulated annealing, threshold accepting and tabu search with an emphasis on their respective key elements.


Non-improving solutions Simulated annealing Thresholding Tabu search 


  1. Aarts, E. H. L., & van Laarhoven, P. J. M. (1985). Statistical cooling: A general approach to combinatorial optimization problems. Philips Journal of Research, 40, 193–226.Google Scholar
  2. Battiti, R., & Tecchiolli, G. (1994). The reactive tabu search. ORSA Journal on Computing, 6, 126–140.CrossRefGoogle Scholar
  3. Conolly, D. T. (1990). An improved simulated annealing technique for the QAP. European Journal of Operational Research, 46, 93–100.CrossRefGoogle Scholar
  4. Dowsland, K. A. (1993). Some experiments with simulated annealing techniques for packing problems. European Journal of Operational Research, 68, 389–399.CrossRefGoogle Scholar
  5. Dowsland, K. A., & Thompson, J. M. (1998). A robust simulated annealing based examination timetabling system. Computers and Operations Research, 25, 637–648.CrossRefGoogle Scholar
  6. Dowsland, K. A., & Thompson, J. M. (2012). Simulated annealing. In G. Rozenberg, T. Back, & J. N. Kok (Eds.), Handbook of natural computing (pp. 1624–1655). Berlin: Springer.Google Scholar
  7. Drezner, Z., & Salhi, S. (2000). Using tabu search for designing one and two ways road networks. Control and Cybernetics Journal, 29, 725–740.Google Scholar
  8. Drezner, Z., & Salhi, S. (2002). Using hybrid metaheuristics for the one-way and two-way network design problem. Naval Research Logistics (NRL), 49, 449–463.CrossRefGoogle Scholar
  9. Drezner, Z., Marcoulides, G. A., & Salhi, S. (1999). Tabu search model selection in multiple regression analysis. Communications in Statistics Simulation and Computation, 28, 349–367.CrossRefGoogle Scholar
  10. Dueck, G. (1993). New optimization heuristics: The great deluge algorithm and the record-to-record travel. Journal of Computational Physics, 104, 86–92.CrossRefGoogle Scholar
  11. Dueck, G., & Scheuer, T. (1990). Threshold accepting: A general purpose optimization algorithm superior to simulated annealing. Journal of Computational Physics, 90, 161–175.CrossRefGoogle Scholar
  12. Eglese, R. (1990). Simulated annealing: A tool for operational research. European Journal of Operational Research, 46, 271–281.CrossRefGoogle Scholar
  13. Gendreau, M., & Potvin, J. Y. (2010). Tabu search. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (pp. 41–59). London: Springer.CrossRefGoogle Scholar
  14. Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers and Operations Research, 13, 533–549.CrossRefGoogle Scholar
  15. Glover, F. (1990). Tabu search: A tutorial. Interfaces, 20, 74–94.CrossRefGoogle Scholar
  16. Glover, F., & Hanafi, S. (2002). Tabu search and finite convergence. Discrete Applied Mathematics, 119, 3–36.CrossRefGoogle Scholar
  17. Glover, F., & Laguna, M. (1997). Tabu search. Boston: Kluwer.CrossRefGoogle Scholar
  18. Hanafi, S., & Freville, A. (1998). An efficient tabu search approach for the 0–1 multidimensional knapsack problem. European Journal of Operational Research, 106, 659–675.CrossRefGoogle Scholar
  19. Hansen, P. (1986). The steepest ascent, mildest descent heuristic for combinatorial programming. Paper presented at the congress on Numerical Methods in Combinatorial Optimization, Capri.Google Scholar
  20. Hu, T. C., Kahng, A. B., & Tsao, C. W. A. (1995). Old bachelor acceptance: A new class of non-monotone threshold accepting methods. ORSA Journal on Computing, 7, 417–425.CrossRefGoogle Scholar
  21. Johnson, D. S., Aragon, C. R., McGeoch, L. A., & Schevon, C. (1989). Optimization by simulated annealing: An experimental evaluation. Part I, graph partitioning. Operations Research, 37, 865–892.CrossRefGoogle Scholar
  22. Kelly, J. P., Golden, B., & Assad, A. A. (1993). Large-scale controlled rounding using tabu search with strategic oscillation. Annals of Operations Research, 41, 69–84.CrossRefGoogle Scholar
  23. Kirkpatrick, S., Gelat, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220, 671–680.CrossRefGoogle Scholar
  24. van Laarhoven, P. J. M., & Aarts, E. H. L. (1987). Simulated annealing: Theory and applications. Rotterdam: Reidel.CrossRefGoogle Scholar
  25. Lee, D. S., Vassiliadis, V. S., & Park, J. M. (2004). A novel threshold accepting meta- heuristic for the job-shop scheduling problem. Computers and Operations Research, 31(13), 2199–2213.CrossRefGoogle Scholar
  26. Li, F., Golden, B., & Wasil, E. (2007). A record-to-record travel algorithm for solving the heterogeneous fleet vehicle routing problem. Computers and Operations Research, 34, 2734–2742.CrossRefGoogle Scholar
  27. Lundy, M., & Mees, A. (1986). Convergence of an annealing algorithm. Mathematical Programming, 34, 111–124.CrossRefGoogle Scholar
  28. Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., & Teller, E. (1953). Equations of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087–1092.CrossRefGoogle Scholar
  29. Osman, I. H., & Christofides, N. (1994). Capacitated clustering problems by hybrid simulated annealing and tabu search. International Transactions in Operational Research, 1, 317–336.CrossRefGoogle Scholar
  30. Osman, I. H., & Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operations Research, 63, 513–623.CrossRefGoogle Scholar
  31. Osman, I. H., & Salhi, S. (1996). Local search strategies for the vehicle fleet mix problem. In V. J. Rayward-Smith, I. H. Osman, C. R. Reeves, & G. D. Smith (Eds.), Modern heuristic search techniques (pp. 131–154). New York: Wiley.Google Scholar
  32. Salhi, S. (2002). Defining tabu list size and aspiration criterion within tabu search methods. Computers and Operations Research, 29, 67–86.CrossRefGoogle Scholar
  33. Skorin-Kapov, J. (1990). Tabu search applied to the quadratic assignment problem. ORSA Journal on Computing, 2, 33–45.CrossRefGoogle Scholar
  34. Tarantilis, C. D., Kiranoudis, C., & Vassiliadis, V. (2003). A list based threshold accepting metaheuristic for the heterogeneous fixed vehicle routing problem. The Journal of the Operational Research Society, 54, 65–71.CrossRefGoogle Scholar
  35. Wassan, N. A. (2006). A reactive tabu search for vehicle routing. The Journal of the Operational Research Society, 57, 111–116.CrossRefGoogle Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Saïd Salhi
    • 1
  1. 1.Centre for Logistics & Heuristic OptimisationKent Business School, University of KentCanterburyUK

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