A Unified View on Hybrid Metaheuristics

  • Günther R. Raidl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4030)


Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to low-level hybrids of different metaheuristics, a unified view based on a common pool template is described. It helps in making similarities and different key components of existing metaheuristics explicit. We then consider these key components as a toolbox for building new, effective hybrid metaheuristics. This approach of thinking seems to be superior to sticking too strongly to the philosophies and historical backgrounds behind the different metaheuristic paradigms. Finally, particularly promising possibilities of combining metaheuristics with constraint programming and integer programming techniques are highlighted.


Local Search Integer Linear Programming Constraint Programming Memetic Algorithm Greedy Randomized Adaptive Search Procedure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  2. 2.
    Glover, F., Kochenberger, G.A.: Handbook of Metaheuristics. Kluwer, Dordrecht (2003)zbMATHGoogle Scholar
  3. 3.
    Hoos, H.H., Stützle, T.: Stochastic Local Search. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  4. 4.
    Glover, F.: Future paths for integer programming and links to artificial intelligence. Decision Sciences 8, 156–166 (1977)CrossRefGoogle Scholar
  5. 5.
    Voß, S., Martello, S., Osman, I.H., Roucairo, C.: Meta-Heuristics: Andvances and Trends in Local Search Paradigms for Optimization. Kluwer, Boston (1999)Google Scholar
  6. 6.
    Blum, C., Roli, A., Sampels, M. (eds.): Proceedings of the First International Workshop on Hybrid Metaheuristics, Valencia, Spain (2004)Google Scholar
  7. 7.
    Blesa, M.J., Blum, C., Roli, A., Sampels, M. (eds.): HM 2005. LNCS, vol. 3636. Springer, Heidelberg (2005)zbMATHGoogle Scholar
  8. 8.
    Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1(1), 67–82 (1997)CrossRefGoogle Scholar
  9. 9.
    Cotta, C.: A study of hybridisation techniques and their application to the design of evolutionary algorithms. AI Communications 11(3–4), 223–224 (1998)Google Scholar
  10. 10.
    Talbi, E.G.: A taxonomy of hybrid metaheuristics. Journal of Heuristics 8(5), 541–565 (2002)CrossRefGoogle Scholar
  11. 11.
    Blum, C., Roli, A., Alba, E.: An introduction to metaheuristic techniques. In: Parallel Metaheuristics, a New Class of Algorithms, pp. 3–42. John Wiley, Chichester (2005)Google Scholar
  12. 12.
    Cotta, C., Talbi, E.G., Alba, E.: Parallel hybrid metaheuristics. In: Alba, E. (ed.) Parallel Metaheuristics, a New Class of Algorithms, pp. 347–370. John Wiley, Chichester (2005)CrossRefGoogle Scholar
  13. 13.
    Puchinger, J., Raidl, G.R.: Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 41–53. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    El-Abd, M., Kamel, M.: A taxonomy of cooperative search algorithms. In: Blesa, M.J., Blum, C., Roli, A., Sampels, M. (eds.) HM 2005. LNCS, vol. 3636, pp. 32–41. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  15. 15.
    Alba, E. (ed.): Parallel Metaheuristics, a New Class of Algorithms. John Wiley, New Jersey (2005)zbMATHGoogle Scholar
  16. 16.
    Moscato, P.: Memetic algorithms: A short introduction. In: Corne, D., et al. (eds.) New Ideas in Optimization, pp. 219–234. McGraw-Hill, New York (1999)Google Scholar
  17. 17.
    Ahuja, R.K., Ergun, Ö., Orlin, J.B., Punnen, A.P.: A survey of very large-scale neighborhood search techniques. Discrete Applied Mathematics 123(1-3), 75–102 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  18. 18.
    Puchinger, J., Raidl, G.R.: Models and algorithms for three-stage two-dimensional bin packing. In: European Journal of Operational Research, Feature Issue on Cutting and Packing (to appear, 2006)Google Scholar
  19. 19.
    Julstrom, B.A.: Strings of weights as chromosomes in genetic algorithms for combinatorial problems. In: Alander, J.T. (ed.) Proceedings of the Third Nordic Workshop on Genetic Algorithms and their Applications, pp. 33–48 (1997)Google Scholar
  20. 20.
    Storer, R.H., Wu, S.D., Vaccari, R.: New search spaces for sequencing problems with application to job-shop scheduling. Management Science 38, 1495–1509 (1992)zbMATHCrossRefGoogle Scholar
  21. 21.
    Glover, F., Laguna, M., Martí, R.: Fundamentals of scatter search and path relinking. Control and Cybernetics 39(3), 653–684 (2000)Google Scholar
  22. 22.
    Applegate, D., Bixby, R., Chvátal, V., Cook, W.: On the solution of the traveling salesman problem. Documenta Mathematica ICM III, 645–656 (1998)Google Scholar
  23. 23.
    Cotta, C., Troya, J.M.: Embedding branch and bound within evolutionary algorithms. Applied Intelligence 18, 137–153 (2003)zbMATHCrossRefGoogle Scholar
  24. 24.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  25. 25.
    Talukdar, S., Baeretzen, L., Gove, A., de Souza, P.: Asynchronous teams: Cooperation schemes for autonomous agents. Journal of Heuristics 4, 295–321 (1998)CrossRefGoogle Scholar
  26. 26.
    Talukdar, S., Murty, S., Akkiraju, R.: Asynchronous teams. In: Handbook of Metaheuristics, vol. 57, pp. 537–556. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  27. 27.
    Denzinger, J., Offermann, T.: On cooperation between evolutionary algorithms and other search paradigms. In: Proceedings of the Congress on Evolutionary Computation 1999, IEEE Press, Los Alamitos (1999)Google Scholar
  28. 28.
    Vaessens, R., Aarts, E., Lenstra, J.: A local search template. In: Manner, R., Manderick, B. (eds.) Parallel Problem Solving from Nature, pp. 67–76. Elsevier, Amsterdam (1992)Google Scholar
  29. 29.
    Calégari, P., Coray, G., Hertz, A., Kobler, D., Kuonen, P.: A taxonomy of evolutionary algorithms in combinatorial optimization. Journal of Heuristics 5(2), 145–158 (1999)zbMATHCrossRefGoogle Scholar
  30. 30.
    Greistorfer, P., Voß, S.: Controlled pool maintenance in combinatorial optimization. In: Rego, C., Alidaee, B. (eds.) Metaheuristic Optimization via Memory and Evolution – Tabu Search and Scatter Search. Operations Research/Computer Science Interfaces, vol. 30, pp. 382–424. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  31. 31.
    Voß, S.: Hybridizing metaheuristics: The road to success in problem solving. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, Springer, Heidelberg (2006), Google Scholar
  32. 32.
    Fink, A., Voß, S.: HotFrame: A heuristic optimization framework. In: Optimization Software Class Libraries. OR/CS Interfaces Series, Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  33. 33.
    Wagner, D.: Eine generische Bibliothek für Metaheuristiken und ihre Anwendung auf das Quadratic Assignment Problem. Master’s thesis, Vienna University of Technology, Institute of Computer Graphics and Algorithms (2005)Google Scholar
  34. 34.
    Voß, S., Woodruff, D.L. (eds.): Optimization Software Class Libraries. OR/CS Interfaces Series. Kluwer Academic Publishers, Dordrecht (2002)zbMATHGoogle Scholar
  35. 35.
    Marriott, K., Stuckey, P.: Programming with Constraints. The MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  36. 36.
    Nemhauser, G., Wolsey, L.: Integer and Combinatorial Optimization. John Wiley, Chichester (1988)zbMATHGoogle Scholar
  37. 37.
    Focacci, F., Laburthe, F., Lodi, A.: Local search and constraint programming. In: Handbook of Metaheuristics, vol. 57, pp. 369–403. Kluwer Academic Publishers, Dordrecht (2003)Google Scholar
  38. 38.
    Puchinger, J., Raidl, G.R., Gruber, M.: Cooperating memetic and branch-and-cut algorithms for solving the multidimensional knapsack problem. In: Proceedings of MIC 2005, the 6th Metaheuristics International Conference, Vienna, Austria, pp. 775–780 (2005)Google Scholar
  39. 39.
    Fischetti, M., Lodi, A.: Local Branching. Mathematical Programming Series B 98, 23–47 (2003)zbMATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Günther R. Raidl
    • 1
  1. 1.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

Personalised recommendations