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A History of Metaheuristics

  • Kenneth Sörensen
  • Marc Sevaux
  • Fred Glover
Living reference work entry

Abstract

This chapter describes the history of metaheuristics in five distinct periods, starting long before the first use of the term and ending a long time in the future.

The field of metaheuristics has undergone several paradigm shifts that have changed the way researchers look upon the development of heuristic methods. Most notably, there has been a shift from the method-centric period, in which metaheuristics were thought of as algorithms, to the framework-centric period, in which researchers think of metaheuristics as more general high-level frameworks, i.e., consistent collections of concepts and ideas that offer guidelines on how to go about solving an optimization problem heuristically.

Tremendous progress has been made in the development of heuristics over the years. Optimization problems that seemed intractable only a few decades ago can now be efficiently solved. Nevertheless, there is still much room for evolution in the research field, an evolution that will allow it to move into the scientific period. In this period, we will see more structured knowledge generation that will benefit both researchers and practitioners.

Unfortunately, a significant fraction of the research community has deluded itself into thinking that scientific progress can be made by resorting to ever more outlandish metaphors as the basis for so-called “novel” methods. Even though considerable damage to the research field will have been inflicted by the time these ideas have been stamped out, there is no doubt that science will ultimately prevail.

Keywords

History 

References

  1. 1.
    Ahuja RK, Ergun Ö, Orlin JB, Punnen AP (2002) A survey of very large-scale neighborhood search techniques. Discret Appl Math 123 (1): 75–102MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Baxter J (1981) Local optima avoidance in depot location. J Oper Res Soc 32:815–819CrossRefGoogle Scholar
  3. 3.
    Carbonell JG (1983) Learning by analogy: formulating and generalizing plans from past experience. In: Machine learning. Springer Science & Business Media, Berlin/Heidelberg, pp 137–161. https://doi.org/10.1007/978-3-662-12405-5_5 Google Scholar
  4. 4.
    Chu T (2014) Human purpose and transhuman potential: a cosmic vision for our future evolution. Origin Press, San Rafael. ISBN:978-1-57983-0250Google Scholar
  5. 5.
    Colorni A, Dorigo M, Maniezzo V (1992) Distributed optimization by ant colonies. In: Varela FJ, Bourgine P (eds) Proceedings of the first European conference on artificial life. MIT Press, Cambridge, pp 134–142Google Scholar
  6. 6.
    Corberán Á, Peiró J, Campos V, Glover F, Martí R (2016) Strategic oscillation for the capacitated hub location problem with modular links. J Heuristics 22 (2): 221–244CrossRefGoogle Scholar
  7. 7.
    Cormen TH, Leiserson CE, Rivest RL, Stein C (2009) Introduction to algorithms, 3rd edn. MIT Press, Cambridge. ISBN:978-0-262-03384-8zbMATHGoogle Scholar
  8. 8.
    Dueck G (1993) New optimization heuristics. J Comput Phys 104 (1): 86–92. https://doi.org/10.1006/jcph.1993.1010 CrossRefzbMATHGoogle Scholar
  9. 9.
    Dueck G, Scheuer T (1990) Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. J Comput Phys 90 (1): 161–175. https://doi.org/10.1016/0021-9991(90)90201-b MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Feo TA, Resende MGC (1995) Greedy randomized adaptive search procedures. J Glob Optim 6 (2): 109–133. https://doi.org/10.1007/bf01096763 MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Fogel LJ, Owens AJ, Walsh MJ (1966) Artificial intelligence through simulated evolution. Wiley, New YorkzbMATHGoogle Scholar
  12. 12.
    García-Martínez C, Rodriguez FJ, Lozano M (2014) Tabu-enhanced iterated greedy algorithm: a case study in the quadratic multiple knapsack problem. Eur J Oper Res 232 (3): 454–463MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Glover F (1977) Heuristics for integer programming using surrogate constraints. Decis Sci 8 (1): 156–166. https://doi.org/10.1111/j.1540-5915.1977.tb01074.x CrossRefGoogle Scholar
  14. 14.
    Glover F (1986) Future paths for integer programming and links to artificial intelligence. Comput Oper Res 13 (5): 533–549. https://doi.org/10.1016/0305-0548(86)90048-1
  15. 15.
    Glover F (1998) A template for scatter search and path relinking. In: Artificial evolution. Lecture notes in computer Science. Springer Science & Business Media, pp 1–51. https://doi.org/10.1007/bfb0026589 Google Scholar
  16. 16.
    Glover F, Laguna M (1997) Tabu search. Kluwer Academic Publishers/Springer, BostonCrossRefzbMATHGoogle Scholar
  17. 17.
    Glover F, Laguna M, Martí R (2000) Fundamentals of scatter search and path relinking. Control and Cybern 29 (3): 653–684MathSciNetzbMATHGoogle Scholar
  18. 18.
    Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, ReadingzbMATHGoogle Scholar
  19. 19.
    Granville V, Krivanek M, Rasson J-P (1994) Simulated annealing: a proof of convergence. IEEE Trans Pattern Anal Mach Intell 16 (6): 652–656. https://doi.org/10.1109/34.295910 CrossRefGoogle Scholar
  20. 20.
    Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann ArborGoogle Scholar
  21. 21.
    Hopfield J (1982) Neural networks and physical systems with emergent collective computational capabilities. Proc Natl Acad Sci 79 (8): 2254–2558MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Hvattum LM, Løkketangen A, Glover F (2004) Adaptive memory search for boolean optimization problems. Discret Appl Math 142 (1): 99–109MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Jarboui B, Derbel H, Hanafi S, Mladenović N (2013) Variable neighborhood search for location routing. Comput Oper Res 40 (1): 47–57. https://doi.org/10.1016/j.cor.2012.05.009 MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220 (4598): 671–680. https://doi.org/10.1126/science.220.4598.671 MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Lourenço HR, Martin OC, Stützle T (2003) Iterated local search. Int Ser Oper Res Manag Sci 57:321–354MathSciNetzbMATHGoogle Scholar
  26. 26.
    Lozano M, Glover F, García-Martínez C, Rodríguez FJ, Martí R (2014) Tabu search with strategic oscillation for the quadratic minimum spanning tree. IIE Trans 46 (4): 414–428CrossRefGoogle Scholar
  27. 27.
    Maniezzo V, Stützle T, Voß S (eds) (2010) Matheuristics. Springer. https://doi.org/10.1007/978-1-4419-1306-7
  28. 28.
    Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24 (11): 1097–1100. https://doi.org/10.1016/s0305-0548(97)00031-2
  29. 29.
    Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts – towards memetic algorithms. Technical Report 826, Caltech Concurrent Computation Program, PasadenaGoogle Scholar
  30. 30.
    Polya G (2014) How to solve it: a new aspect of mathematical method. Princeton university press, PrincetonzbMATHGoogle Scholar
  31. 31.
    Rechenberg I (1989) Evolution strategy: nature’s way of optimization. In: Optimization: methods and applications, possibilities and limitations. Springer Science & Business Media, Berlin/New York, pp 106–126. https://doi.org/10.1007/978-3-642-83814-9_6 CrossRefGoogle Scholar
  32. 32.
    Ribeiro CC, Rosseti I, Souza RC (2011) Effective probabilistic stopping rules for randomized metaheuristics: GRASP implementations. In: Learning and intelligent optimization. Lecture notes in computer science, vol 6683. Springer Science & Business Media, pp 146–160. https://doi.org/10.1007/978-3-642-25566-3_11
  33. 33.
    Ropke S, Pisinger D (2006) An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transp Sci 40 (4): 455–472CrossRefGoogle Scholar
  34. 34.
    Ruiz R, Stützle T (2008) An iterated greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives. Eur J Oper Res 187 (3): 1143–1159CrossRefzbMATHGoogle Scholar
  35. 35.
    Shore HH (1970) The transportation problem and the VOGEL approximation method. Decis Sci 1 (3-4): 441–457. https://doi.org/10.1111/j.1540-5915.1970.tb00792.x CrossRefGoogle Scholar
  36. 36.
    Simon HA (1996) The sciences of the artificial, 3rd edn. MIT Press, CambridgeGoogle Scholar
  37. 37.
    Simon HA, Newell A (1958) Heuristic problem solving: the next advance in operations research. Oper Res 6 (1): 1–10. https://doi.org/10.1287/opre.6.1.1 CrossRefGoogle Scholar
  38. 38.
    Sörensen K (2015) Metaheuristics–the metaphor exposed. Int Trans Oper Res 22 (1): 3–18. https://doi.org/10.1111/itor.12001 MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Sörensen K, Glover FW (2013) Metaheuristics. In: Gass SI, Fu MC (eds) Encyclopedia of operations research and management science, 663 3rd edn, pp 960–970, Springer, Boston, MACrossRefGoogle Scholar
  40. 40.
    Watson J-P, Barbulescu L, Whitley LD, Howe AE (2002) Contrasting structured and random permutation flow-shop scheduling problems: search-space topology and algorithm performance. INFORMS J Comput 14 (2): 98–123. https://doi.org/10.1287/ijoc.14.2.98.120 MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Watson J-P, Beck JC, Howe AE, Whitley LD (2003) Problem difficulty for tabu search in job-shop scheduling. Artif Intell 143 (2): 189–217. https://doi.org/10.1016/s0004-3702(02)00363-6
  42. 42.
    Weyland D (2010) A rigorous analysis of the harmony search algorithm. Int J Appl Metaheuristic Comput 1 (2): 50–60. https://doi.org/10.4018/jamc.2010040104 CrossRefGoogle Scholar
  43. 43.
    Yagiura M, Iwasaki S, Ibaraki T, Glover F (2004) A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem. Discret Optim 1 (1): 87–98CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.University of AntwerpAntwerpBelgium
  2. 2.Université de Bretagne-SudLorientFrance
  3. 3.OptTek Systems, IncBoulderUSA

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