Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Meta-heuristics: The State of the Art

  • Conference paper
  • First Online:
Local Search for Planning and Scheduling (LSPS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2148))

Included in the following conference series:

Abstract

Meta-heuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of meta-heuristics. This primarily focuses on the significant progress which general frames within the meta-heuristics field have implied for solving combinatorial optimization problems, mainly those for planning and scheduling.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E.H.L. Aarts and J.K. Lenstra, editors. Local Search in Combinatorial Optimization. Wiley, Chichester, 1997.

    MATH  Google Scholar 

  2. E.H.L. Aarts and M. Verhoeven. Local search. In M. Dell’Amico, F. Maffioli, and S. Martello, editors, Annotated Bibliographies in Combinatorial Optimization, pages 163–180. Wiley, Chichester, 1997.

    Google Scholar 

  3. R.K. Ahuja, O. Ergun, J.B. Orlin, and A.B. Punnen. A survey of very large-scale neighborhood search techniques. Working paper, Sloan School of Management, MIT, 1999.

    Google Scholar 

  4. E.J. Anderson, C.A. Glass, and C.N. Potts. Machine scheduling. In E.H.L. Aarts and J.K. Lenstra, editors, Local Search in Combinatorial Optimization, pages 361–414. Wiley, Chichester, 1997.

    Google Scholar 

  5. A.A. Andreatta, S.E.R. Carvalho, and C.C. Ribeiro. An object-oriented framework for local search heuristics. In Proceedings of the 26th Conference on Technology of Object-Oriented Languages and Systems (TOOLS USA’98), pages 33–45. IEEE, Piscataway, 1998.

    Google Scholar 

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

    MATH  Google Scholar 

  7. R.S. Barr, B.L. Golden, J.P. Kelly, M.G.C. Resende, and W.R. Stewart. Designing and reporting on computational experiments with heuristic methods. Journal of Heuristics, 1:9–32, 1995.

    Article  MATH  Google Scholar 

  8. R. Battiti. Reactive search: Toward self-tuning heuristics. In V.J. Rayward-Smith, I.H. Osman, C.R. Reeves, and G.D. Smith, editors, Modern Heuristic Search Methods, pages 61–83. Wiley, Chichester, 1996.

    Google Scholar 

  9. D.P. Bertsekas and D.A. Castanon. Rollout algorithms for stochastic scheduling problems. Journal of Heuristics, 5:89–108, 1999.

    Article  MATH  Google Scholar 

  10. D.P. Bertsekas, J.N. Tsitsiklis, and C. Wu. Rollout algorithms for combinatorial optimization. Journal of Heuristics, 3:245–262, 1997.

    Article  MATH  Google Scholar 

  11. J.C. Bezdek. What is Computational Intelligence. In J.M. Zurada, R.J. Marks II, and C.J. Robinson, editors, Computational Intelligence: Imitating Life, pages 1–12. IEEE Press, New York, 1994.

    Google Scholar 

  12. Y. Caseau, F. Laburthe, and G. Silverstein. A meta-heuristic factory for vehicle routing problems. In J. Jaffar, editor, Principles and Practice of Constraint Programming-CP’ 99, Lecture Notes in Computer Science 1713, pages 144–158. Springer, Berlin, 1999.

    Google Scholar 

  13. I. Charon and O. Hudry. The noising method: A new method for combinatorial optimization. Operations Research Letters, 14:133–137, 1993.

    Article  MATH  MathSciNet  Google Scholar 

  14. K.M.F. Choi, J.H.M. Lee, and P.J. Stuckey. A Lagrangian reconstruction of GENET. Artificial Intelligence, 123:1–39, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  15. T.G. Crainic, M. Toulouse, and M. Gendreau. Toward a taxonomy of parallel tabu search heuristics. INFORMS Journal on Computing, 9:61–72, 1997.

    MATH  Google Scholar 

  16. B. de Backer, V. Furnon, P. Shaw, P. Kilby, and P. Prosser. Solving vehicle routing problems using constraint programming and metaheuristics. Journal of Heuristics, 6:501–523, 2000.

    Article  MATH  Google Scholar 

  17. M. Dorigo, V. Maniezzo, and A. Colorni. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, B-26:29–41, 1996.

    Google Scholar 

  18. A. Dowsland. Simulated annealing. In C. Reeves, editor, Modern Heuristic Techniques for Combinatorial Problems, pages 20–69. Halsted, Blackwell, 1993.

    Google Scholar 

  19. 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 

  20. C.W. Duin and S. Voβ. Steiner tree heuristics-a survey. In H. Dyckhoff, U. Derigs, M. Salomon, and H.C. Tijms, editors, Operations Research Proceedings 1993, pages 485–496, Berlin, 1994. Springer.

    Google Scholar 

  21. C.W. Duin and S. Voβ. The pilot method: A strategy for heuristic repetition with application to the Steiner problem in graphs. Networks, 34:181–191, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  22. T.A. Feo, K. Venkatraman, and J.F. Bard. A GRASP for a difficult single machine scheduling problem. Computers & Operations Research, 18:635–643, 1991.

    Article  MATH  Google Scholar 

  23. A. Fink and S. Voβ. Applications of modern heuristic search methods to continuous flow-shop scheduling problems. Working paper, Technische Universität Braunschweig, Germany, 1999.

    Google Scholar 

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

    Article  Google Scholar 

  25. A. Fink, S. Voβ, and D.L. Woodruff. An adoption path for intelligent heuristic search componentware. In E. Rolland and N.S. Umanath, editors, Proceedings of the 4th INFORMS Conference on Information Systems and Technology, pages 153–168. INFORMS, Linthicum, 1999.

    Google Scholar 

  26. C. Fleurent and J.A. Ferland. Object-oriented implementation of heuristic search methods for graph coloring, maximum clique, and satisfiability. In D.S. Johnson and M.A. Trick, editors, Cliques, Coloring, and Satisfiability: Second DIM ACS Implementation Challenge, volume 26 of DIM ACS Series in Discrete Mathematics and Theoretical Computer Science, pages 619–652. AMS, Princeton, 1996.

    Google Scholar 

  27. D.B. Fogel. On the philosophical differences between evolutionary algorithms and genetic algorithms. In D.B. Fogel and W. Atmar, editors, Proceedings of the Second Annual Conference on Evolutionary Programming, pages 23–29. Evolutionary Programming Society, La Jolla, 1993.

    Google Scholar 

  28. D.B. Fogel. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence. IEEE Press, New York, 1995.

    Google Scholar 

  29. L.M. Gambardella and M. Dorigo. An ant colony system hybridized with a new local search for the sequential ordering problem. INFORMS Journal on Computing, 12:237–255, 2000.

    Article  MATH  MathSciNet  Google Scholar 

  30. M.R. Garey and D.S. Johnson. Computers and Intractability, A Guide to the Theory of NP-Completeness. Freeman, New York, 1979.

    MATH  Google Scholar 

  31. F. Glover. Heuristics for integer programming using surrogate constraints. Decision Sciences, 8:156–166, 1977.

    Article  Google Scholar 

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

    Article  MATH  MathSciNet  Google Scholar 

  33. F. Glover. Tabu search-Part II. ORSA Journal on Computing, 2:4–32, 1990.

    MATH  Google Scholar 

  34. F. Glover. Scatter search and star-paths: beyond the genetic metaphor. OR Spektrum, 17:125–137, 1995.

    Article  MATH  Google Scholar 

  35. F. Glover. Tabu search and adaptive memory programming-Advances, applications and challenges. In R.S. Barr, R.V. Helgason, and J.L. Kennington, editors, Interfaces in Computer Science and Operations Research: Advances in Metaheuristics, Optimization, and Stochastic Modeling Technologies, pages 1–75. Kluwer, Boston, 1997.

    Google Scholar 

  36. F. Glover, editor. Tabu Search Methods for Optimization. European Journal of Operational Research 106:221–692. Elsevier, Amsterdam, 1998.

    Google Scholar 

  37. F. Glover and M. Laguna. Tabu Search. Kluwer, Boston, 1997.

    MATH  Google Scholar 

  38. D.E. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, 1989.

    MATH  Google Scholar 

  39. S. Grolimund and J.-G. Ganascia. Driving tabu search with case-based reasoning. European Journal of Operational Research, 103:326–338, 1997.

    Article  MATH  Google Scholar 

  40. T. Grünert. Lagrangean tabu search. In C.C. Ribeiro, editor, Third Metaheuristics International Conference: Extended Abstracts, pages 263–267, 1999.

    Google Scholar 

  41. J. Gu. The Multi-SAT algorithm. Discrete Applied Mathematics, 96–97:111–126, 1999.

    Article  MathSciNet  Google Scholar 

  42. P. Hansen and N. Mladenović. An introduction to variable neighborhood search. In S. Voβ, S. Martello, I.H. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pages 433–458. Kluwer, Boston, 1999.

    Google Scholar 

  43. J.P. Hart and A.W. Shogan. Semi-greedy heuristics: An empirical study. Operations Research Letters, 6:107–114, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  44. W. Harvey and M. Ginsberg. Limited discrepancy search. In Proceedings of the 14th IJCAI, pages 607–615, San Mateo, 1995. Morgan Kaufmann.

    Google Scholar 

  45. S. Heipcke. Comparing constraint programming and mathematical programming approaches to discrete optimisation-the change problem. Journal of the Operational Research Society, 50:581–595, 1999.

    Article  MATH  Google Scholar 

  46. 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 

  47. F. Hoffmeister and T. Bäck. Genetic algorithms and evolution strategies: Similarities and differences. In H.-P. Schwefel and R. Männer, editors, Parallel Problem Solving from Nature-PPSN I, Lecture Notes in Computer Science 496, pages 455–469. Springer, Berlin, 1991.

    Chapter  Google Scholar 

  48. J.H. Holland. Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, 1975.

    Google Scholar 

  49. J.N. Hooker. Testing heuristics: We have it all wrong. Journal of Heuristics, 1:33–42, 1995.

    Article  MATH  Google Scholar 

  50. J.N. Hooker. Constraint satisfaction methods for generating valid cuts. In D.L. Woodruff, editor, Advances in Computational and Stochastic Optimization, Logic Programming, and Heuristic Search, pages 1–30. Kluwer, Boston, 1998.

    Google Scholar 

  51. H.H. Hoos and T. Stützle. Evaluating Las Vegas algorithms-Pitfalls and remedies. In Proceedings of UAI-98, pages 238–245. 1998.

    Google Scholar 

  52. H.H. Hoos and T. Stützle. Local search algorithms for SAT. Journal of Automated Reasoning, 24:421–481, 2000.

    Article  MATH  Google Scholar 

  53. HOTFRAME/Heuristic Optimization Framework. http://www.winforms.phil.tubs.de/research/hotframe.htm, 2000.

  54. L. Ingber. Adaptive simulated annealing (ASA): Lessons learned. Control and Cybernetics, 25:33–54, 1996.

    MATH  Google Scholar 

  55. J. Jaffar, editor. Principles and Practice of Constraint Programming-CP’ 99. Lecture Notes in Computer Science 1713. Springer, Berlin, 1999.

    MATH  Google Scholar 

  56. Y. Jiang, H. Kautz, and B. Selman. Solving problems with hard and soft constraints using a stochastic algorithm for MAX-SAT. Technical report, AT&T Bell Laboratories, 1995.

    Google Scholar 

  57. D.S. Johnson, C.R. Aragon, L.A. McGeoch, and C. Schevon. Optimization by simulated annealing: An experimental evaluation; part i, graph partitioning. Operations Research, 37:865–892, 1989.

    MATH  Google Scholar 

  58. M.S. Jones, G.P. McKeown, and V.J. Rayward-Smith. Distribution, cooperation, and hybridization for combinatorial optimization. Technical report, University of East Anglia, Norwich, 2000.

    Google Scholar 

  59. A.B. Kahng and G. Robins. A new class of iterative Steiner tree heuristics with good performance. IEEE Transactions on Computer-Aided Design, 11:893–902, 1992.

    Article  Google Scholar 

  60. H. Kautz and B. Selman. Pushing the envelope: Planning, propositional logic, and stochastic search. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-96), pages 1194–1201. 1996.

    Google Scholar 

  61. H. Kautz, Selman, and Y. Jiang. General stochastic approach to solving problems with hard and soft constraints. In D. Gu, J. Du, and P. Pardalos, editors, The Satisfiability Problem: Theory and Applications, DIMACS Series in Discrete Mathematics and Theoretical Computer Science 35, pages 573–586. AMS, Providence, 1997.

    Google Scholar 

  62. S. Kirkpatrick, C.D. Gelatt Jr., and M.P. Vecchi. Optimization by simulated annealing. Science, 220:671–680, 1983.

    Article  MathSciNet  Google Scholar 

  63. G. Laporte and I.H. Osman, editors. Metaheuristics in Combinatorial Optimization. Annals of Operations Research 63. Baltzer, Amsterdam, 1996.

    Google Scholar 

  64. J.L. Lauriere. A language and a program for stating and solving combinatorial problems. Artificial Intelligence, 10:29–127, 1978.

    Article  MATH  MathSciNet  Google Scholar 

  65. S. Lin and B.W. Kernighan. An effective heuristic algorithm for the traveling-salesman problem. Operations Research, 21:498–516, 1973.

    MATH  MathSciNet  Google Scholar 

  66. C. McGeoch. Toward an experimental method for algorithm simulation. INFORMS Journal on Computing, 8:1–15, 1996.

    MATH  Google Scholar 

  67. Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin, 3rd edition, 1999.

    Google Scholar 

  68. Z. Michalewicz and D.B. Fogel. How to Solve It: Modern Heuristics. Springer, Berlin, 1999.

    Google Scholar 

  69. L. Michel and P. van Hentenryck. LOCALIZER: A modeling language for local search. INFORMS Journal on Computing, 11:1–14, 1999.

    MATH  MathSciNet  Google Scholar 

  70. L. Michel and P. van Hentenryck. Localizer. Constraints, 5:43–84, 2000.

    Article  MATH  Google Scholar 

  71. P. Moscato. An introduction to population approaches for optimization and hierarchical objective functions: A discussion on the role of tabu search. Annals of Operations Research, 41:85–121, 1993.

    Article  MATH  Google Scholar 

  72. H. Mühlenbein. Genetic algorithms. In E.H.L. Aarts and J.K. Lenstra, editors, Local Search in Combinatorial Optimization, pages 137–171. Wiley, Chichester, 1997.

    Google Scholar 

  73. H. Müller-Merbach. Heuristics and their design: a survey. European Journal of Operational Research, 8:1–23, 1981.

    Article  MATH  Google Scholar 

  74. A. Nareyek. Using Global Constraints for Local Search. In E. C. Freuder and R. J. Wallace, editors, Constraint Programming and Large Scale Discrete Optimization, DIMACS Volume 57, pages 9–28. American Mathematical Society Publications, Providence, 2001.

    Google Scholar 

  75. A. Nareyek. Beyond the Plan-Length Criterion. In A. Nareyek, editor, Local Search for Planning and Scheduling. Springer LNAI 2048, Berlin, 2001. (this volume)

    Google Scholar 

  76. I.H. Osman. Heuristics for the generalized assignment problem: simulated annealing and tabu search approaches. OR Spektrum, 17:211–225, 1995.

    Article  MATH  Google Scholar 

  77. I.H. Osman and J.P. Kelly, editors. Meta-Heuristics: Theory and Applications. Kluwer, Boston, 1996.

    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. J. Pearl. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley, Reading, 1984.

    Google Scholar 

  80. G. Pesant and M. Gendreau. A constraint programming framework for local search methods. Journal of Heuristics, 5:255–279, 1999.

    Article  MATH  Google Scholar 

  81. E. Pesch and F. Glover. TSP ejection chains. Discrete Applied Mathematics, 76:165–182, 1997.

    Article  MATH  MathSciNet  Google Scholar 

  82. E. Pesch and S. Voβ, editors. Applied Local Search. OR Spektrum 17:55–225. Springer, Berlin, 1995.

    Google Scholar 

  83. G. Polya. How to solve it. Princeton University Press, Princeton, 1945.

    MATH  Google Scholar 

  84. C. Potts and S. van de Velde. Dynasearch-iterative local improvement by dynamic programming. Technical report, University of Twente, 1995.

    Google Scholar 

  85. V.J. Rayward-Smith, editor. Applications of Modern Heuristic Methods Waller, Henley-on-Thames, 1995.

    Google Scholar 

  86. V.J. Rayward-Smith, I.H. Osman, C.R. Reeves, and G.D. Smith, editors. Modern Heuristic Search Methods. Wiley, Chichester, 1996.

    MATH  Google Scholar 

  87. C.R. Reeves, editor. Modern Heuristic Techniques for Combinatorial Problems. Blackwell, Oxford, 1993.

    MATH  Google Scholar 

  88. C.C. Ribeiro, E. Uchoa, and R.F. Werneck. A hybrid GRASP with perturbations for the Steiner problem in graphs. Technical report, Department of Computer Science, Catholic University of Rio de Janeiro, 2000.

    Google Scholar 

  89. L.-M. Rousseau, M. Gendreau, and G. Pesant. Using constraint-based operators to solve the vehicle routing problem with time windows. Technical report, CRT, University of Montreal, Canada, 2000.

    Google Scholar 

  90. S.M. Sait and H. Youssef. Iterative Computer Algorithms with Applications in Engineering: Solving Combinatorial Optimization Problems. IEEE Computer Society Press, Los Alamitos, 1999.

    MATH  Google Scholar 

  91. M. Sakawa and T. Shibano. Multiobjective fuzzy satisficing methods for 0-1 knapsack problems through genetic algorithms. In W. Pedrycz, editor, Fuzzy Evolutionary Computation, pages 155–177. Kluwer, Boston, 1997.

    Google Scholar 

  92. D. Schuurmans and F. Southey. Local search characteristics of incomplete SAT procedures. In Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-2000), pages 297–302. 2000.

    Google Scholar 

  93. H.-P. Schwefel and T. Bäck. Artificial evolution: How and why? In D. Quagliarella, J. Périaux, C. Poloni, and G. Winter, editors, Genetic Algorithms and Evolution Strategy in Engineering and Computer Science: Recent Advances and Industrial Applications, pages 1–19. Wiley, Chichester, 1998.

    Google Scholar 

  94. B. Selman, H. Kautz, and B. Cohen. Noise strategies for improving local search. In Proceedings of the 11th National Conference on Artificial Intelligence (AAAI-94), pages 337–343. 1994.

    Google Scholar 

  95. B. Selman, H. Levesque, and D. Mitchell. A new method for solving hard satisfiability problems. In Proceedings of the 9th National Conference on Artificial Intelligence (AAAI-92), pages 440–446. 1992.

    Google Scholar 

  96. P. Shaw. Using constraint programming and local search methods to solve vehicle routing problems. Working paper, ILOG S.A., Gentilly, France, 1998.

    Google Scholar 

  97. K. Smith. Neural networks for combinatorial optimisation: A review of more than a decade of research. INFORMS Journal on Computing, 11:15–34, 1999.

    Article  MATH  MathSciNet  Google Scholar 

  98. L. Sondergeld. Performance Analysis Methods for Heuristic Search Optimization with an Application to Cooperative Agent Algorithms. Shaker, Aachen, 2001.

    Google Scholar 

  99. R.H. Storer, S.D. Wu, and R. Vaccari. Problem and heuristic space search strategies for job shop scheduling. ORSA Journal on Computing, 7:453–467, 1995.

    MATH  Google Scholar 

  100. T. Stützle and H. Hoos. The max-min ant system and local search for combinatorial optimization problems. In S. Voss, S. Martello, I.H. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pages 313–329. Kluwer, Boston, 1999.

    Google Scholar 

  101. E. Taillard. An introduction to ant systems. In M. Laguna and J.L. Gonzalez-Velarde, editors, Computing Tools for Modeling, Optimization and Simulation, pages 131–144. Kluwer, Boston, 2000.

    Google Scholar 

  102. E. Taillard and S. Voβ. Popmusic. Working paper, University of Applied Sciences of Western Switzerland, 1999.

    Google Scholar 

  103. E.D. Taillard, L.M. Gambardella, M. Gendreau, and J.-Y. Potvin. Adaptive memory programming: A unified view of meta-heuristics. Technical Report IDSIA-19-98, Istituto Dalle Molle di Studi sull’Intelligenza Artificiale, Lugano, 1998.

    Google Scholar 

  104. R.J.M. Vaessens, E.H.L. Aarts, and J.K. Lenstra. A local search template. Computers & Operations Research, 25:969–979, 1998.

    Article  MATH  MathSciNet  Google Scholar 

  105. P. van Hentenryck. Constraint solving for combinatorial search problems: A tutorial. In U. Montanari and F. Rossi, editors, Principles and Practice of Constraint Programming-CP’ 95, Lecture Notes in Computer Science 976, pages 564–587. Springer, Berlin, 1995.

    Google Scholar 

  106. M.G.A. Verhoeven and E.H.L. Aarts. Parallel local search techniques. Journal of Heuristics, 1:43–65, 1995.

    Article  MATH  Google Scholar 

  107. R.V.V. Vidal, editor. Applied Simulated Annealing. Lecture Notes in Economics and Mathematical Systems 396. Springer, Berlin, 1993.

    MATH  Google Scholar 

  108. S. Voβ. Intelligent Search. Manuscript, TU Darmstadt, 1993.

    Google Scholar 

  109. S. Voβ. Tabu search: applications and prospects. In D.-Z. Du and P. Pardalos, editors, Network Optimization Problems, pages 333–353. World Scientific, Singapore, 1993.

    Google Scholar 

  110. S. Voβ. Observing logical interdependencies in tabu search: Methods and results. In V.J. Rayward-Smith, I.H. Osman, C.R. Reeves, and G.D. Smith, editors, Modern Heuristic Search Methods, pages 41–59, Chichester, 1996. Wiley.

    Google Scholar 

  111. S. Voβ, S. Martello, I.H Osman, and C. Roucairol, editors. Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer, Boston, 1999.

    Google Scholar 

  112. J.P. Walser. Integer Optimization by Local Search. Lecture Notes in Artificial Intelligence 1637. Springer, Berlin, 1999.

    MATH  Google Scholar 

  113. D. Whitley, S. Rana, J. Dzubera, and K.E. Mathias. Evaluating evolutionary algorithms. Artificial Intelligence, 85:245–276, 1996.

    Article  Google Scholar 

  114. D.H. Wolpert and W.G. Macready. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1:67–82, 1997.

    Article  Google Scholar 

  115. D.L. Woodruff. A class library for heuristic search optimization. INFORMS Computer Science Technical Section Newsletter, 18(2):1–5, 1997.

    MathSciNet  Google Scholar 

  116. D.L. Woodruff. Proposals for chunking and tabu search. European Journal of Operational Research, 106:585–598, 1998.

    Article  MATH  Google Scholar 

  117. D.L. Woodruff. A chunking based selection strategy for integrating meta-heuristics with branch and bound. In S. Voβ, S. Martello, I.H. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, pages 499–511. Kluwer, Boston, 1999.

    Google Scholar 

  118. S.H. Zanakis, J.R. Evans, and A.A. Vazacopoulos. Heuristic methods and applications: a categorized survey. European Journal of Operational Research, 43:88–110, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  119. H.-J. Zimmermann. Fuzzy Set Theory and its Applications. Kluwer, Boston, 2nd edition, 1991.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut für Wirtschaftswissenschaften, Technische Universität Braunschweig, Abt-Jerusalem-Straβe 7, D-38106, Braunschweig, Germany

    Stefan Voß

Authors
  1. Stefan Voß
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. GMD FIRST, Kekuléstr. 7, 12489, Berlin, Germany

    Alexander Nareyek

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Voß, S. (2001). Meta-heuristics: The State of the Art. In: Nareyek, A. (eds) Local Search for Planning and Scheduling. LSPS 2000. Lecture Notes in Computer Science(), vol 2148. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45612-0_1

Download citation

  • DOI: https://doi.org/10.1007/3-540-45612-0_1

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42898-5

  • Online ISBN: 978-3-540-45612-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation