Advertisement

Improvement-Only Heuristics

  • Saïd Salhi
Chapter
  • 996 Downloads

Abstract

In this chapter, I only consider those approaches that accept an improving solution that can be generated from one iteration to the next. Basic neighbourhood definitions with simple examples are first provided. This is followed by the descent or greedy method while emphasising some of its drawbacks. I then present some of the efficient hill climbing methods commonly used such as GRASP, a simple composite heuristic, multi-level, variable neighbourhood and perturbation schemes. A brief on large neighbourhood search, iterated local search and guided local search will also be given.

Keywords

Improvement only heuristics Hill climbing GRASP VNS Multi-level Perturbation 

References

  1. Ahuja, R. K., Ergun, O., Orlin, J. B., & Punnen, A. P. (2002). A survey of very large scale neighbourhood search techniques. Discrete Applied Mathematics, 123, 75–102.CrossRefGoogle Scholar
  2. Brimberg, J., Drezner, Z., Mladenović, N., & Salhi, S. (2014). A new local search for continuous location problems. European Journal of Operational Research, 232, 256–265.CrossRefGoogle Scholar
  3. Brimberg, J., Mladenović, N., Todosijević, R., & Urosević, D. (2015). Nested variable neighbourhood search. SYM-OP-IS, XLII Int Conf on Oper Res, September16–19, Ivanjica.Google Scholar
  4. Charon, I., & Hudry, O. (1993). The noising method- a new method for combinatorial optimization. Operations Research Letters, 14, 133–137.CrossRefGoogle Scholar
  5. Charon, I., & Hudry, O. (2009). Self-tuning of the noising method. Optimzation, 58, 1–21.CrossRefGoogle Scholar
  6. Elshaikh, A., Salhi, S., Brimberg, J., Mladenović, N., Callaghan, B., & Nagy, G. (2016). An Adaptive perturbation-based heuristic: An application to the continuous p-centre problem. Computers and Operations Research, 75, 1–11.CrossRefGoogle Scholar
  7. Feo, T. A., & Resende, M. G. C. (1989). A probablistic heuristic for a computationally difficult set covering problem. Operations Research Letters, 8, 67–71.CrossRefGoogle Scholar
  8. Feo, T. A., & Resende, M. G. C. (1995). Greedy randomized adaptive search procedures. Journal of Global Optimization, 6, 109–133.CrossRefGoogle Scholar
  9. 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
  10. Hansen, P., Mladenović, N., Brimberg, J., & Moreno Perez, J. A. (2010). Variable neighbourhood search. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (pp. 61–86). London: Springer.CrossRefGoogle Scholar
  11. Imran, A. (2008). An adaptation of metaheuristics for the single and the multiple depots heterogeneous fleet vehicle routing problems. PhD thesis, University of Kent, Canterbury.Google Scholar
  12. Lin, S. (1965). Computer solutions of the travelling salesman problem. Bell Systems Technical Journal, 44, 2244–2269.CrossRefGoogle Scholar
  13. Lourenco, H. R., Martin, O. C., & Stutzle, T. (2010). Iterated local search: Framework and applications. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (pp. 363–397). London: Springer.CrossRefGoogle Scholar
  14. Luis, M., Salhi, S., & Gabor, N. (2011). A guided reactive GRASP for the capacitated multi-source Weber problem. Computers and Operations Research, 38, 1014–1024.CrossRefGoogle Scholar
  15. Marti, R. (2003). Multi-start methods. In F. Glover & G. A. Kochenberger (Eds.), Handbook of metaheuristics (pp. 355–368). London: Kluwer.CrossRefGoogle Scholar
  16. Mladenović, N., & Hansen, P. (1997). Variable neighbourhood search. Computers and Operations Research, 24, 1097–1100.CrossRefGoogle Scholar
  17. Mladenović, N., Plastria, F., & Urosević, D. (2005). Reformulation descent applied to circle packing problems. Computers and Operations Research, 32, 2419–2434.CrossRefGoogle Scholar
  18. Mladenović, N., Todosijević, R., & Urosević, D. (2014). Two level general variable neighbourhood search for attractive travelling salesman problem. Computers and Operations Research, 52, 341–348.CrossRefGoogle Scholar
  19. Nagy, G., & Salhi, S. (1996). Nested location routing heuristic using route length approximation. Studies in Locational Analysis, 8, 3–22.Google Scholar
  20. Resende, M. G. C., & Ribiero, C. G. (2010). Greedy randomized adaptive search procedures: Advances, hybridisations, and applications. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristics (pp. 283–319). London: Springer.CrossRefGoogle Scholar
  21. Salhi, S. (1997). A perturbation heuristic for a class of location problem. The Journal of the Operational Research Society, 48, 1233–1240.CrossRefGoogle Scholar
  22. Salhi, S., & Rand, G. K. (1987). Improvements to vehicle routing heuristics. The Journal of the Operational Research Society, 38, 293–295.CrossRefGoogle Scholar
  23. Salhi, S., & Sari, M. (1997). A Multi-level composite heuristic for the multi-depot vehicle fleet mix problem. European Journal of Operational Research, 103, 78–95.CrossRefGoogle Scholar
  24. Schrimpf, G., Schneider, J., Stamm-Wilbrabdt, H., & Dueck, H. (2000). Record breaking optimization results- using the ruin and recreate principle. Journal of Computational Physics, 159, 139–171.CrossRefGoogle Scholar
  25. Shaw, P. (1998). Using constraint programming and local search methods to solve vehicle routing problem. In CP-98 (fourth international conference in principles and practice of constraints programming). Lecture Notes in computer Science, 1520, 417–431.CrossRefGoogle Scholar
  26. Voudouris, C., & Tsang, E. P. K. (2010). Guided local search. In M. Gendreau & J. Y. Potvin (Eds.), Handbook of metaheuristcs (pp. 321–361). London: Springer.CrossRefGoogle Scholar
  27. Wassan, N., Wassan, N.A., Nagy, G., & Salhi, S. (2016). The Multiple trip vehicle routing problem with backhauls: Formulation and a two-level variable neighbourhood search. Computers and Operations Research. doi: 10.1016/j.cor.2015.12.07.Google Scholar
  28. Zainuddin, Z. M., & Salhi, S. (2007). A perturbation-based heuristic for the capacitated multisource Weber problem. European Journal of Operational Research, 179, 1194–1207.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

Personalised recommendations