Decomposing Metaheuristic Operations

  • Richard Senington
  • David DukeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8241)


Non-exhaustive local search methods are fundamental tools in applied branches of computing such as operations research, and in other applications of optimisation. These problems have proven stubbornly resistant to attempts to find generic meta-heuristic toolkits that are both expressive and computationally efficient for the large problem spaces involved. This paper complements recent work on functional abstractions for local search by examining three fundamental operations on the states that characterise allowable and/or intermediate solutions. We describe how three fundamental operations are related, and how these can be implemented effectively as part of a functional local search library.


Search Optimisation Stochastic Combinatorial 



The authors would like to thank Tim Sheard for all his advice in the final stages of writing this paper.


  1. 1.
    Hoos, H., Stützle, T.: Stochastic Local Search: Foundations & Applications. Morgan Kaufmann Publishers Inc., San Francisco (2005)Google Scholar
  2. 2.
    Contardo, C., Cordeau, J.-F., Gendron, B.: A grasp + ilp-based metaheuristic for the capacitated location-routing problem. Technical report (2011)Google Scholar
  3. 3.
    Prins, C., Prodhon, C., Ruiz, A., Soriano, P., Calvo, R.W.: Solving the capacitated location-routing problem by a cooperative lagrangean relaxation-granular tabu search heuristic. Transp. Sci. 41(4), 470–483 (2007)CrossRefGoogle Scholar
  4. 4.
    Masrom, S., Siti, A.Z., Hashimah, P.N., Rahman, A.A.: Towards rapid development of user defined metaheuristics hybridisation. Int. J. Softw. Eng. Appl. 5(2), 1–12 (2011)Google Scholar
  5. 5.
    Senington, R., Duke, D.: Combinators for meta-heuristic search. J. Funct. Program. (2012, Submitted)Google Scholar
  6. 6.
    Merz, P., Freisleben, B.: Memetic algorithms for the travelling salesman problem. Complex Syst. 13(4), 297–345 (2001)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimisation. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)CrossRefGoogle Scholar
  8. 8.
    Reinelt, G.: TSPLIB - a traveling salesman problem library. INFORMS J. Comput. 3(4), 376–384 (1991).
  9. 9.
    Li, J., Kwan, R.S.K.: A fuzzy genetic algorithm for driver scheduling. Eur. J. Oper. Res. 147(2), 334–344 (2003)CrossRefzbMATHGoogle Scholar
  10. 10.
    Burke, E., Hart, E., Kendall, G., Newall, J., Ross, P., Schulenburg, S.: Hyper-heuristics: an emerging direction in modern search technology. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, pp. 457–474. Kluwer, Dordrecht (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of LeedsLeedsUK

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