Skip to main content
Springer Nature Link
Log in

Hybrid Metaheuristics: An Introduction

  • Chapter
Hybrid Metaheuristics

Part of the book series: Studies in Computational Intelligence ((SCI,volume 114))

In many real life settings, high quality solutions to hard optimization problems such as flight scheduling or load balancing in telecommunication networks are required in a short amount of time. Due to the practical importance of optimization problems for industry and science, many algorithms to tackle them have been developed. One important class of such algorithms are metaheuristics. The field of metaheuristic research has enjoyed a considerable popularity in the last decades. In this introductory chapter we first provide a general overview on metaheuristics. Then we turn towards a new and highly successful branch of metaheuristic research, namely the hybridization of metaheuristics with algorithmic components originating from other techniques for optimization. The chapter ends with an outline of the remaining book chapters.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. E. H. L. Aarts, J. H. M. Korst, and P. J. M. van Laarhoven. Simulated annealing. In E. H. L. Aarts and J. K. Lenstra, editors, Local Search in Combinatorial Optimization, pages 91–120. John Wiley & Sons, Chichester, UK, 1997.

    Google Scholar 

  2. E. H. L. Aarts and J. K. Lenstra, editors. Local Search in Combinatorial Optimization. John Wiley & Sons, Chichester, UK, 1997.

    MATH  Google Scholar 

  3. E. Alba, editor. Parallel Metaheuristics: A New Class of Algorithms. John Wiley, 2005.

    Google Scholar 

  4. T. Bäck. Evolutionary Algorithms in Theory and Practice. Oxford University Press, New York, 1996.

    MATH  Google Scholar 

  5. T. Bäck, D. B. Fogel, and Z. Michalewicz, editors. Handbook of Evolutionary Computation. Institute of Physics Publishing Ltd, Bristol, UK, 1997.

    MATH  Google Scholar 

  6. R. Battiti and M. Protasi. Reactive Search, a history-base heuristic for MAX-SAT. ACM Journal of Experimental Algorithmics, 2:Article 2, 1997.

    Google Scholar 

  7. R. Battiti and G. Tecchiolli. The Reactive Tabu Search. ORSA Journal on Computing, 6(2):126–140, 1994.

    MATH  Google Scholar 

  8. S. Binato, W. J. Hery, D. Loewenstern, and M. G. C. Resende. A greedy randomized adaptive search procedure for job shop scheduling. In P. Hansen and C. C. Ribeiro, editors, Essays and surveys on metaheuristics, pages 59–79. Kluwer Academic Publishers, 2001.

    Google Scholar 

  9. C. Blum. Ant colony optimization. Physics of Life Reviews, 2(4):353–373, 2005.

    Article  MathSciNet  Google Scholar 

  10. C. Blum. Beam-ACO—Hybridizing ant colony optimization with beam search: An application to open shop scheduling. Computers & Operations Research, 32(6):1565–1591, 2005.

    Article  Google Scholar 

  11. C. Blum and A. Roli. Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys, 35(3):268–308, 2003.

    Article  Google Scholar 

  12. S. Boettcher and A. G. Percus. Optimization with extremal dynamics. Complexity, 8:57–62, 2003.

    Article  MathSciNet  Google Scholar 

  13. P. Calégary, G. Coray, A. Hertz, D. Kobler, and P. Kuonen. A taxonomy of evolutionary algorithms in combinatorial optimization. Journal of Heuristics, 5:145–158, 1999.

    Article  Google Scholar 

  14. V. Černý. A thermodynamical approach to the travelling salesman problem: An efficient simulation algorithm. Journal of Optimization Theory and Applications, 45:41–51, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  15. P. Chardaire, J. L. Lutton, and A. Sutter. Thermostatistical persistency: A powerful improving concept for simulated annealing algorithms. European Journal of Operational Research, 86:565–579, 1995.

    Article  MATH  Google Scholar 

  16. C. A. Coello Coello. An Updated Survey of GA-Based Multiobjective Optimization Techniques. ACM Computing Surveys, 32(2):109–143, 2000.

    Article  Google Scholar 

  17. D. T. Connolly. An improved annealing scheme for the QAP. European Journal of Operational Research, 46:93–100, 1990.

    Article  MATH  MathSciNet  Google Scholar 

  18. T. G. Crainic and M. Toulouse. Introduction to the special issue on Parallel Meta-Heuristics. Journal of Heuristics, 8(3):247–249, 2002.

    Article  Google Scholar 

  19. T. G. Crainic and M. Toulouse. Parallel Strategies for Meta-heuristics. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics, volume 57 of International Series in Operations Research & Management Science. Kluwer Academic Publishers, Norwell, MA, 2002.

    Google Scholar 

  20. F. Della Croce and V. T’kindt. A Recovering Beam Search algorithm for the one machine dynamic total completion time scheduling problem. Journal of the Operational Research Society, 53(11):1275–1280, 2002.

    Article  MATH  Google Scholar 

  21. M. Dell’Amico, A. Lodi, and F. Maffioli. Solution of the Cumulative Assignment Problem with a well–structured Tabu Search method. Journal of Heuristics, 5:123–143, 1999.

    Article  MATH  Google Scholar 

  22. M. L. den Besten, T. Stützle, and M. Dorigo. Design of iterated local search algorithms: An example application to the single machine total weighted tardiness problem. In E. J. W. Boers, J. Gottlieb, P. L. Lanzi, R. E. Smith, S. Cagnoni, E. Hart, G. R. Raidl, and H. Tijink, editors, Applications of Evolutionary Computing: Proceedings of EvoWorkshops 2001, volume 2037 of Lecture Notes in Computer Science, pages 441–452. Springer-Verlag, Berlin, Germany, 2001.

    Chapter  Google Scholar 

  23. J.-L. Deneubourg, S. Aron, S. Goss, and J.-M. Pasteels. The self-organizing exploratory pattern of the argentine ant. Journal of Insect Behaviour, 3:159–168, 1990.

    Article  Google Scholar 

  24. J. Denzinger and T. Offerman. On cooperation between evolutionary algorithms and other search paradigms. In Proceedings of Congress on Evolutionary Computation – CEC’1999, pages 2317–2324, 1999.

    Google Scholar 

  25. M. Dorigo and L. M. Gambardella. Ant Colony System: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1):53–66, 1997.

    Article  Google Scholar 

  26. M. Dorigo and T. Stützle. http://www.metaheuristics.net/, 2000. Visited in January 2003.

  27. M. Dorigo and T. Stützle. Ant Colony Optimization. MIT Press, Cambridge, MA, 2004.

    MATH  Google Scholar 

  28. G. Dueck. New Optimization Heuristics. Journal of Computational Physics, 104:86–92, 1993.

    Article  MATH  Google Scholar 

  29. G. Dueck and T. Scheuer. Threshold Accepting: A General Purpose Optimization Algorithm Appearing Superior to Simulated Annealing. Journal of Computational Physics, 90:161–175, 1990.

    Article  MATH  MathSciNet  Google Scholar 

  30. W. Feller. An Introduction to Probability Theory and its Applications. John Whiley, 1968.

    Google Scholar 

  31. T. A. Feo and M. G. C. Resende. Greedy randomized adaptive search procedures. Journal of Global Optimization, 6:109–133, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  32. P. Festa and M. G. C. Resende. GRASP: An annotated bibliography. In C. C. Ribeiro and P. Hansen, editors, Essays and Surveys on Metaheuristics, pages 325–367. Kluwer Academic Publishers, 2002.

    Google Scholar 

  33. A. Fink and S. Voß. Generic metaheuristics application to industrial engineering problems. Computers & Industrial Engineering, 37:281–284, 1999.

    Article  Google Scholar 

  34. M. Fleischer. Simulated Annealing: past, present and future. In C. Alexopoulos, K. Kang, W. R. Lilegdon, and G. Goldsman, editors, Proceedings of the 1995 Winter Simulation Conference, pages 155–161, 1995.

    Google Scholar 

  35. F. Focacci, F. Laburthe, and A. Lodi. Local Search and Constraint Programming. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics, volume 57 of International Series in Operations Research & Management Science. Kluwer Academic Publishers, Norwell, MA, 2002.

    Google Scholar 

  36. D. B. Fogel. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 5(1):3–14, 1994.

    Article  Google Scholar 

  37. G. B. Fogel, V. W. Porto, D. G. Weekes, D. B. Fogel, R. H. Griffey, J. A. McNeil, E. Lesnik, D. J. Ecker, and R. Sampath. Discovery of RNA structural elements using evolutionary computation. Nucleic Acids Research, 30(23):5310–5317, 2002.

    Article  Google Scholar 

  38. L. J. Fogel. Toward inductive inference automata. In Proceedings of the International Federation for Information Processing Congress, pages 395–399, Munich, 1962.

    Google Scholar 

  39. L. J. Fogel, A. J. Owens, and M. J. Walsh. Artificial Intelligence through Simulated Evolution. Wiley, 1966.

    Google Scholar 

  40. C. Fonlupt, D. Robilliard, P. Preux, and E. G. Talbi. Fitness landscapes and performance of meta-heuristics. In S. Voß, S. Martello, I. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer Academic Publishers, 1999.

    Google Scholar 

  41. L. M. Gambardella and M. Dorigo. Ant Colony System hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing, 12(3):237–255, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  42. M. R. Garey and D. S. Johnson. Computers and intractability; a guide to the theory of NP-completeness. W. H. Freeman, 1979.

    Google Scholar 

  43. M. Gendreau, G. Laporte, and J.-Y. Potvin. Metaheuristics for the capacitated VRP. In P. Toth and D. Vigo, editors, The Vehicle Routing Problem, volume 9 of SIAM Monographs on Discrete Mathematics and Applications, pages 129–154. SIAM, Philadelphia, 2002.

    Google Scholar 

  44. F. Glover. Heuristics for Integer Programming Using Surrogate Constraints. Decision Sciences, 8:156–166, 1977.

    Article  Google Scholar 

  45. F. Glover. Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13:533–549, 1986.

    Article  MATH  MathSciNet  Google Scholar 

  46. F. Glover. Tabu Search Part II. ORSA Journal on Computing, 2(1):4–32, 1990.

    MATH  Google Scholar 

  47. F. Glover and M. Laguna. Tabu Search. Kluwer Academic Publishers, 1997.

    Google Scholar 

  48. D. E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison Wesley, Reading, MA, 1989.

    MATH  Google Scholar 

  49. J. J. Grefenstette. A user’s guide to GENESIS 5.0. Technical report, Navy Centre for Applied Research in Artificial Intelligence, Washington D.C., USA, 1990.

    Google Scholar 

  50. P. Hansen. The steepest ascent mildest descent heuristic for combinatorial programming. In Congress on Numerical Methods in Combinatorial Optimization, Capri, Italy, 1986.

    Google Scholar 

  51. P. Hansen and N. Mladenović. Variable Neighborhood Search for the p-Median. Location Science, 5:207–226, 1997.

    Article  MATH  Google Scholar 

  52. P. Hansen and N. Mladenović. An introduction to variable neighborhood search. In S. Voß, S. Martello, I. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, chapter 30, pages 433–458. Kluwer Academic Publishers, 1999.

    Google Scholar 

  53. P. Hansen and N. Mladenović. Variable neighborhood search: Principles and applications. European Journal of Operational Research, 130:449–467, 2001.

    Article  MATH  MathSciNet  Google Scholar 

  54. A. Hertz and D. Kobler. A framework for the description of evolutionary algorithms. European Journal of Operational Research, 126:1–12, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  55. T. Hogg and C. P. Williams. Solving the really hard problems with cooperative search. In Proceedings of AAAI93, pages 213–235. AAAI Press, 1993.

    Google Scholar 

  56. J. H. Holland. Adaption in natural and artificial systems. The University of Michigan Press, Ann Harbor, MI, 1975.

    MATH  Google Scholar 

  57. H. H. Hoos and T. Stützle. Stochastic Local Search: Foundations and Applications. Elsevier, Amsterdam, The Netherlands, 2004.

    Google Scholar 

  58. T. Ibaraki and K. Nakamura. Packing problems with soft rectangles. In F. Almeida, M. Blesa, C. Blum, J. M. Moreno, M. Pérez, A. Roli, and M. Sampels, editors, Proceedings of HM 2006 – 3rd International Workshop on Hybrid Metaheuristics, volume 4030 of Lecture Notes in Computer Science, pages 13–27. Springer-Verlag, Berlin, Germany, 2006.

    Chapter  Google Scholar 

  59. L. Ingber. Adaptive simulated annealing (ASA): Lessons learned. Control and Cybernetics – Special Issue on Simulated Annealing Applied to Combinatorial Optimization, 25(1):33–54, 1996.

    MATH  Google Scholar 

  60. D. S. Johnson and L. A. McGeoch. The traveling salesman problem: a case study. In E. H. L. Aarts and J. K. Lenstra, editors, Local Search in Combinatorial Optimization, pages 215–310. John Wiley & Sons, Chichester, UK, 1997.

    Google Scholar 

  61. T. Jones. Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, Univ. of New Mexico, Albuquerque, NM, 1995.

    Google Scholar 

  62. P. Kilby, P. Prosser, and P. Shaw. Guided Local Search for the Vehicle Routing Problem with time windows. In S. Voß, S. Martello, I. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pages 473–486. Kluwer Academic Publishers, 1999.

    Google Scholar 

  63. S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671–680, 1983.

    Article  MathSciNet  Google Scholar 

  64. H. R. Lourenço, O. Martin, and T. Stützle. A beginner’s introduction to Iterated Local Search. In Proceedings of MIC’2001 – Meta–heuristics International Conference, volume 1, pages 1–6, 2001.

    Google Scholar 

  65. H. R. Lourenço, O. Martin, and T. Stützle. Iterated local search. In F. Glover and G. Kochenberger, editors, Handbook of Metaheuristics, volume 57 of International Series in Operations Research & Management Science, pages 321–353. Kluwer Academic Publishers, Norwell, MA, 2002.

    Google Scholar 

  66. V. Maniezzo. Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem. INFORMS Journal on Computing, 11(4):358–369, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  67. O. Martin and S. W. Otto. Combining Simulated Annealing with Local Search Heuristics. Annals of Operations Research, 63:57–75, 1996.

    Article  MATH  Google Scholar 

  68. O. Martin, S. W. Otto, and E. W. Felten. Large-step markov chains for the traveling salesman problem. Complex Systems, 5(3):299–326, 1991.

    MATH  MathSciNet  Google Scholar 

  69. D. Merkle, M. Middendorf, and H. Schmeck. Ant Colony Optimization for Resource-Constrained Project Scheduling. IEEE Transactions on Evolutionary Computation, 6(4):333–346, 2002.

    Article  Google Scholar 

  70. N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller, and E. Teller. Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21:1087–1092, 1953.

    Article  Google Scholar 

  71. Z. Michalewicz and M. Michalewicz. Evolutionary computation techniques and their applications. In Proceedings of the IEEE International Conference on Intelligent Processing Systems, pages 14–24, Beijing, China, 1997. Institute of Electrical & Electronics Engineers, Incorporated.

    Google Scholar 

  72. M. Milano and A. Roli. MAGMA: A multiagent architecture for metaheuristics. IEEE Trans. on Systems, Man and Cybernetics – Part B, 34(2):925–941, 2004.

    Article  Google Scholar 

  73. P. Mills and E. Tsang. Guided Local Search for solving SAT and weighted MAX-SAT Problems. In Ian Gent, Hans van Maaren, and Toby Walsh, editors, SAT2000, pages 89–106. IOS Press, 2000.

    Google Scholar 

  74. M. Mitchell. An introduction to genetic algorithms. MIT press, Cambridge, MA, 1998.

    MATH  Google Scholar 

  75. P. Moscato. Memetic algorithms: A short introduction. In F. Glover D. Corne and M. Dorigo, editors, New Ideas in Optimization. McGraw-Hill, 1999.

    Google Scholar 

  76. G. L. Nemhauser and A. L. Wolsey. Integer and Combinatorial Optimization. John Wiley & Sons, New York, 1988.

    MATH  Google Scholar 

  77. E. Nowicki and C. Smutnicki. A fast taboo search algorithm for the job-shop problem. Management Science, 42(2):797–813, 1996.

    Article  MATH  Google Scholar 

  78. I. H. Osman and G. Laporte. Metaheuristics: A bibliography. Annals of Operations Research, 63:513–623, 1996.

    Article  MATH  MathSciNet  Google Scholar 

  79. P. S. Ow and T. E. Morton. Filtered beam search in scheduling. International Journal of Production Research, 26:297–307, 1988.

    Article  Google Scholar 

  80. C. H. Papadimitriou and K. Steiglitz. Combinatorial Optimization – Algorithms and Complexity. Dover Publications, Inc., New York, 1982.

    MATH  Google Scholar 

  81. L. S. Pitsoulis and M. G. C. Resende. Greedy Randomized Adaptive Search procedure. In P. M. Pardalos and M. G. C. Resende, editors, Handbook of Applied Optimization, pages 168–183. Oxford University Press, 2002.

    Google Scholar 

  82. M. Prais and C. C. Ribeiro. Reactive GRASP: An application to a matrix decomposition problem in TDMA traffic assignment. INFORMS Journal on Computing, 12:164–176, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  83. S. Prestwich. Combining the Scalability of Local Search with the Pruning Techniques of Systematic Search. Annals of Operations Research, 115:51–72, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  84. S. Prestwich and A. Roli. Symmetry breaking and local search spaces. In Proceedings of CPAIOR 2005, volume 3524 of Lecture Notes in Computer Science. Springer-Verlag, Berlin, Germany, 2005.

    Google Scholar 

  85. J. Puchinger and G. R. Raidl. Combining metaheuristics and exact algorithms in combinatorial optimization: A survey and classification. In J. Mira and J. R. Álvarez, editors, Proceedings of the First International Work-Conference on the Interplay Between Natural and Artificial Computation, volume 3562 of Lecture Notes in Computer Science, pages 41–53. Springer-Verlag, Berlin, Germany, 2005.

    Google Scholar 

  86. G. R. Raidl. A unified view on hybrid metaheuristics. In F. Almeida, M. Blesa, C. Blum, J. M. Moreno, M. Pérez, A. Roli, and M. Sampels, editors, Proceedings of HM 2006 – 3rd International Workshop on Hybrid Metaheuristics, volume 4030 of Lecture Notes in Computer Science, pages 1–12. Springer-Verlag, Berlin, Germany, 2006.

    Chapter  Google Scholar 

  87. I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, 1973.

    Google Scholar 

  88. C. R. Reeves, editor. Modern Heuristic Techniques for Combinatorial Problems. Blackwell Scientific Publishing, Oxford, England, 1993.

    MATH  Google Scholar 

  89. C. R. Reeves and J. E. Rowe. Genetic Algorithms: Principles and Perspectives. A Guide to GA Theory. Kluwer Academic Publishers, Boston (USA), 2002.

    Google Scholar 

  90. M. G. C. Resende and C. C. Ribeiro. A GRASP for graph planarization. Networks, 29:173–189, 1997.

    Article  MATH  Google Scholar 

  91. C. C. Ribeiro and M. C. Souza. Variable neighborhood search for the degree constrained minimum spanning tree problem. Discrete Applied Mathematics, 118:43–54, 2002.

    Article  MATH  MathSciNet  Google Scholar 

  92. A. Roli. Symmetry-breaking and local search: A case study. In SymCon’04 – 4th International Workshop on Symmetry and Constraint Satisfaction Problems. 2004.

    Google Scholar 

  93. A. Schaerf, M. Cadoli, and M. Lenzerini. LOCAL++: A C++ framework for local search algorithms. Software Practice & Experience, 30(3):233–257, 2000.

    Article  Google Scholar 

  94. G. R. Schreiber and O. C. Martin. Cut size statistics of graph bisection heuristics. SIAM Journal on Optimization, 10(1):231–251, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  95. P. Shaw. Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. In M. Maher and J.-F. Puget, editors, Principle and Practice of Constraint Programming – CP98, volume 1520 of Lecture Notes in Computer Science, pages 417–431. Springer-Verlag, 1998.

    Google Scholar 

  96. A. Shmygelska and H. H. Hoos. An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics, 6(30):1–22, 2005.

    Google Scholar 

  97. M. Sipper, E. Sanchez, D. Mange, M. Tomassini, A. Pérez-Uribe, and A. Stauffer. A Phylogenetic, Ontogenetic, and Epigenetic View of Bio-Inspired Hardware Systems. IEEE Transactions on Evolutionary Computation, 1(1):83–97, 1997.

    Article  Google Scholar 

  98. L. Sondergeld and S. Voß. Cooperative intelligent search using adaptive memory techniques. In S. Voß, S. Martello, I. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, chapter 21, pages 297–312. Kluwer Academic Publishers, 1999.

    Google Scholar 

  99. W. M. Spears, K. A. De Jong, T. Bäck, D. B. Fogel, and H. de Garis. An overview of evolutionary computation. In P. B. Brazdil, editor, Proceedings of the European Conference on Machine Learning (ECML-93), volume 667, pages 442–459, Vienna, Austria, 1993. Springer-Verlag.

    Google Scholar 

  100. P. F. Stadler. Landscapes and their correlation functions. Journal of Mathematical Chemistry, 20:1–45, 1996. Also available as SFI preprint 95-07-067.

    Google Scholar 

  101. T. Stützle. Local Search Algorithms for Combinatorial Problems – Analysis, Algorithms and New Applications. DISKI – Dissertationen zur Künstliken Intelligenz. infix, Sankt Augustin, Germany, 1999.

    Google Scholar 

  102. T. Stützle and H. H. Hoos. \({\cal M}{\cal A}{\cal X}\)-\({\cal M}{\cal I}{\cal N}\) Ant System. Future Generation Computer Systems, 16(8):889–914, 2000.

    Google Scholar 

  103. É. D. Taillard. Robust Taboo Search for the Quadratic Assignment Problem. Parallel Computing, 17:443–455, 1991.

    Article  MathSciNet  Google Scholar 

  104. E.-G. Talbi. A Taxonomy of Hybrid Metaheuristics. Journal of Heuristics, 8(5):541–564, 2002.

    Article  Google Scholar 

  105. M. Toulouse, T. G. Crainic, and B. Sansò. An experimental study of the systemic behavior of cooperative search algorithms. In S. Voß, S. Martello, I. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, chapter 26, pages 373–392. Kluwer Academic Publishers, 1999.

    Google Scholar 

  106. D. Urošević, J. Brimberg, and N. Mladenović. Variable neighborhood decomposition search for the edge weighted k-cardinality tree problem. Computers & Operations Research, 31:1205–1213, 2004.

    Article  MATH  MathSciNet  Google Scholar 

  107. P. J. M. Van Laarhoven, E. H. L. Aarts, and J. K. Lenstra. Job Shop Scheduling by Simulated Annealing. Operations Research, 40:113–125, 1992.

    Article  MATH  MathSciNet  Google Scholar 

  108. M. D. Vose. The simple genetic algorithm: foundations and theory. MIT Press, Cambridge, MA, 1999.

    MATH  Google Scholar 

  109. S. Voß, S. Martello, I. H. Osman, and C. Roucairol, editors. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1999.

    MATH  Google Scholar 

  110. S. Voß and D. Woodruff, editors. Optimization Software Class Libraries. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002.

    MATH  Google Scholar 

  111. C. Voudouris. Guided Local Search for Combinatorial Optimization Problems. PhD thesis, Department of Computer Science, University of Essex, 1997. pp. 166.

    Google Scholar 

  112. C. Voudouris and E. Tsang. Guided Local Search. European Journal of Operational Research, 113(2):469–499, 1999.

    Article  MATH  Google Scholar 

  113. A. S. Wade and V. J. Rayward-Smith. Effective local search for the Steiner tree problem. Studies in Locational Analysis, 11:219–241, 1997. Also in Advances in Steiner Trees, ed. by Ding-Zhu Du, J. M.Smith and J. H. Rubinstein, Kluwer, 2000.

    Google Scholar 

  114. D. Whitley. The GENITOR algorithm and selective pressure: Why rank-based allocation of reproductive trials is best. In Proceedings of the 3rd International Conference on Genetic Algorithms, ICGA 1989, pages 116–121. Morgan Kaufmann Publishers, 1989.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 112, E-08034, Barcelona, Spain

    Dr. Christian Blum

  2. Alma Mater Studiorum, Università di Bologna, Via Venezia 52, I-47023, Cesena, Italy

    Dr. Andrea Roli

Authors
  1. Dr. Christian Blum
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dr. Andrea Roli
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 112, E-08034, Barcelona, Spain

    Christian Blum

  2. Dept. Llenguatges i Sistemes Informátics, Universitat Politècnica de Catalunya, Jordi Girona 1-3 Omega 213, E-08034, Barcelona, Spain

    Maria José Blesa Aguilera

  3. Università di Bologna, Via Venezia 52, I-47023, Cesena, Italy

    Andrea Roli

  4. Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, B-1050, Brussels, Belgium

    Michael Sampels

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Blum, C., Roli, A. (2008). Hybrid Metaheuristics: An Introduction. In: Blum, C., Aguilera, M.J.B., Roli, A., Sampels, M. (eds) Hybrid Metaheuristics. Studies in Computational Intelligence, vol 114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78295-7_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78295-7_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78294-0

  • Online ISBN: 978-3-540-78295-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics