Advertisement

Iterated Local Search

  • Helena R. Lourenço
  • Olivier C. Martin
  • Thomas Stützle
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 57)

Keywords

Local Search Tabu Search Acceptance Criterion Variable Neighborhood Search Local Search Algorithm 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    D. Applegate, R. Bixby, V. Chvátal and W. Cook (2000) Finding tours in the TSP. Preliminary version of a book chapter available via www.keck.caam.rice.edu/concorde.html.Google Scholar
  2. [2]
    D. Applegate, W. Cook and A. Rohe (1999) Chained Lin-Kernighan for large traveling salesman problems. Technical Report No. 99887, Forschungsinstitut fürDiskrete Mathematik, University of Bonn, Germany.Google Scholar
  3. [3]
    T. Bäck (1996) Evolutionary Algorithms in Theory and Practice. Oxford University Press.Google Scholar
  4. [4]
    E. Balas and A. Vazacopoulos (1998) Guided local search with shifting bottleneck for job shop scheduling. Management Science, 44(2), 262–275.Google Scholar
  5. [5]
    R. Battiti and A. Bertossi (1999) Greedy, prohibition, and reactive heuristics for graph-partitioning. IEEE Transactions on Computers, 48(4), 361–385.CrossRefGoogle Scholar
  6. [6]
    R. Battiti and M. Protasi (1997) Reactive search, a history-based heuristic for MAX-SAT. ACM Journal of Experimental Algorithmics, 2.Google Scholar
  7. [7]
    R. Battiti and G. Tecchiolli (1994) The reactive tabu search. ORSA Journal on Computing, 6(2), 126–140.Google Scholar
  8. [8]
    E.B. Baum (1986) Iterated descent: A better algorithm for local search in combinatorial optimization problems. Technical report, Caltech, Pasadena, CA. manuscript.Google Scholar
  9. [9]
    E.B. Baum (1986) Towards practical “neural” computation for combinatorial optimization problems. In: J. Denker (ed.), Neural Networks for Computing. AIP conference proceedings, pp. 53–64.Google Scholar
  10. [10]
    J. Baxter (1981) Local optima avoidance in depot location. Journal of the Operational Research Society, 32, 815–819.Google Scholar
  11. [11]
    J.L. Bentley (1992) Fast algorithms for geometric traveling salesman problems. ORSA Journal on Computing, 4(4), 387–411.zbMATHMathSciNetGoogle Scholar
  12. [12]
    P. Brucker, J. Hurink and F. Werner (1996) Improving local search heuristics for some scheduling problems—part I. Discrete Applied Mathematics, 65(1–3), 97–122.MathSciNetGoogle Scholar
  13. [13]
    P. Brucker, J. Hurink and F. Werner (1997) Improving local search heuristics for some scheduling problems—part II. Discrete Applied Mathematics, 72(1–2), 47–69.MathSciNetGoogle Scholar
  14. [14]
    S.A. Canute, M.G.C. Resende and C.C. Ribeiro (2000) Local search with perturbations for the prize-collecting steiner tree problem in graphs. Networks (submitted).Google Scholar
  15. [15]
    J. Carlier (1982) The one-machine sequencing problem European Journal of Operational Research, 11, 42–47.CrossRefzbMATHMathSciNetGoogle Scholar
  16. [16]
    V. Cerny (1985) A thermodynamical approach to the traveling salesman problem. Journal of Optimization Theory and Applications, 45(1), 41–51.zbMATHMathSciNetGoogle Scholar
  17. [17]
    N. Christofides (1976) Worst-case analysis of a new heuristic for the travelling salesman problem. Technical Report 388, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, PA.Google Scholar
  18. [18]
    B. Codenotti, G. Manzini, L. Margara and G. Resta (1996) Perturbation: An efficient technique for the solution of very large instances of the Euclidean TSP. INFORMS Journal on Computing, 8, 125–133.Google Scholar
  19. [19]
    R.K. Congram, C.N. Potts and S.L. Van de Velde (2000) An iterated dynasearch algorithm for the single-machine total weighted tardiness scheduling problem. INFORMS Journal on Computing (to appear).Google Scholar
  20. [20]
    H.A.J. Crauwels, C.N. Potts and L.N. Van Wassenhove (1998) Local search heuristics for the single machine total weighted tardiness scheduling problem. INFORMS Journal on Computing, 10(3), 341–350.MathSciNetGoogle Scholar
  21. [21]
    M. Dorigo and G. Di Caro (1999) The ant colony optimization meta-heuristic. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw Hill, pp. 11–32.Google Scholar
  22. [22]
    T.A. Feo and M.G.C. Resende (1995) Greedy randomized adaptive search procedures. Journal of Global Optimization, 6, 109–133.CrossRefMathSciNetGoogle Scholar
  23. [23]
    C. Fonlupt, D. Robilliard, P. Preux and E.-G. Talbi (1999) Fitness landscape and performance of meta-heuristics. In: S. Voss, S. Martello, I.H. Osman and C. Roucairol (eds.), Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer Academic Publishers, Boston, MA, pp. 257–268.Google Scholar
  24. [24]
    C. Glass and C. Potts (1996) A comparison of local search methods for flow shop scheduling. Annals of Operations Research, 63, 489–509.CrossRefGoogle Scholar
  25. [25]
    F. Glover (1986) Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13(5), 533–549.zbMATHMathSciNetGoogle Scholar
  26. [26]
    F. Glover (1989) Tabu search—part I. ORSA Journal on Computing, 1(3), 190–206.zbMATHGoogle Scholar
  27. [27]
    F. Glover (1990) Tabu search—part II. ORSA Journal on Computing, 2( 1), 4–32.zbMATHGoogle Scholar
  28. [28]
    F. Glover (1995) Tabu thresholding: Improved search by nonmonotonic trajectories. ORSA Journal on Computing, 7(4), 426–442.zbMATHMathSciNetGoogle Scholar
  29. [29]
    F. Glover (1996) Finding a best traveling salesman 4-opt move in the same time as a best 2-opt move. Journal of Heuristics, 2, 169–179.CrossRefzbMATHGoogle Scholar
  30. [30]
    F. Glover (1999) Scatter search and path relinking. In: D. Corne, M. Dorigo and F. Glover (eds.), New Ideas in Optimization. McGraw Hill, pp. 297–316.Google Scholar
  31. [31]
    F. Glover and M. Laguna (1997) Tabu Search. Kluwer Academic Publishers, Boston, MA.Google Scholar
  32. [32]
    M.X. Goemans and D.P. Williamson (1996) The primal dual method for approximation algorithms and its application to network design problems. In: D. Hochbaum (ed.), Approximation Algorithms for NP-hard Problems. PWS Publishing, pp. 144–191.Google Scholar
  33. [33]
    P. Hansen and N. Mladenović (1999) An introduction to variable neighborhood search. In: S. Voss, S. Martello, I.H. Osman and C. Roucairol (eds.), Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization. Kluwer Academic Publishers, Boston, MA, pp. 433–58.Google Scholar
  34. [34]
    R. Haupt (1989) A survey of priority rule-based scheduling. OR Spektrum, 11, 3–6.CrossRefzbMATHMathSciNetGoogle Scholar
  35. [35]
    I. Hong, A.B. Kahng and B.R. Moon (1997) Improved large-step Markov chain variants for the symmetric TSP. Journal of Heuristics, 3(1), 63–81.CrossRefGoogle Scholar
  36. [36]
    T.C. Hu, A.B. Kahng and C.-W.A. Tsao (1995) Old bachelor acceptance: A new class of non-monotone threshold accepting methods. ORSA Journal on Computing, 7(4), 417–425.Google Scholar
  37. [37]
    D.S. Johnson (1990) Local optimization and the travelling salesman problem. In: Proceedings of the 17th Colloquium on Automata, Languages, and Programming, volume 443 of LNCS, Springer Verlag, Berlin, pp. 446–461.Google Scholar
  38. [38]
    D.S. Johnson and L.A. McGeoch (1997) The travelling salesman problem: A case study in local optimization. In: E.H.L. Aarts and J.K. Lenstra (eds.), Local Search in Combinatorial Optimization. John Wiley & Sons, Chichester, England, pp. 215–310.Google Scholar
  39. [39]
    K. Katayama and H. Narihisa (1999) Iterated local search approach using genetic transformation to the traveling salesman problem. In: Proceedings of GECCO’99, Vol. 1. Morgan Kaufmann, pp. 321–328.Google Scholar
  40. [40]
    B.W. Kernighan and S. Lin (1970) An efficient heuristic procedure forpartitioning graphs. Bell Systems Technology Journal, 49, 213–219.Google Scholar
  41. [41]
    S. Kirkpatrick, C.D. Gelatt Jr. and M.P. Vecchi (1983) Optimization by simulated annealing. Science, 220, 671–680.MathSciNetGoogle Scholar
  42. [42]
    S. Kreipl (2000) A large step random walk for minimizing total weighted tardiness in ajob shop. Journal of Scheduling, 3(3), 125–138.CrossRefzbMATHMathSciNetGoogle Scholar
  43. [43]
    S. Lin and B.W. Kernighan (1973) An effective heuristic algorithm for the travelling salesman problem. Operations Research, 21, 498–516.MathSciNetGoogle Scholar
  44. [44]
    H.R. Lourenço (1995) Job-shop scheduling: Computational study of local search and large-step optimization methods. European Journal of Operational Research, 83, 347–364.CrossRefzbMATHGoogle Scholar
  45. [45]
    H.R. Lourenço (1998) A polynomial algorithm for a special case of the one-machine scheduling problem with time-lags. Technical Report Economic Working Papers Series, No. 339, Universitat Pompeu Fabra. Journal of Scheduling (submitted).Google Scholar
  46. [46]
    H.R. Lourenço and M. Zwijnenburg (1996) Combining the large-step optimization with tabu-search: Application to the job-shop scheduling problem. In: I.H. Osman and J.P. Kelly (eds.), Meta-Heuristics: Theory & Applications. Kluwer Academic Publishers, pp. 219–236.Google Scholar
  47. [47]
    O. Martin and S.W. Otto (1995) Partitoning of unstructured meshes for load balancing. Concurrency: Practice and Experience, 7, 303–314.Google Scholar
  48. [48]
    O. Martin and S.W. Otto (1996) Combining simulated annealing with local search heuristics. Annals of Operations Research, 63, 57–75.Google Scholar
  49. [49]
    O. Martin, S.W. Otto and E.W. Felten (1991) Large-step Markov chains for the traveling salesman problem. Complex Systems, 5(3), 299–326.MathSciNetGoogle Scholar
  50. [50]
    O. Martin, S.W. Otto and E.W. Felten (1992) Large-step Markov chains for the TSP incorporating local search heuristics. Operations Research Letters, 11, 219–224.CrossRefMathSciNetGoogle Scholar
  51. [51]
    P. Merz and B. Freisleben (2000) Fitness landscapes, memetic algorithms and greedy operators for graph bi-partitioning. Evolutionary Computation, 8(1), 61–91.CrossRefGoogle Scholar
  52. [52]
    N. Metropolis, A. Rosenbluth, M. Rosenbluth, A. Teller and M. Teller (1953) Equation of state calculations for fast computing machines. Journal of Chemical Physics, 21, 1087–1092.CrossRefGoogle Scholar
  53. [53]
    M. Mézard, G. Parisi and M.A. Virasoro (1987) Spin-Glass Theory and Beyond, volume 9 of Lecture Notes in Physics. World Scientific, Singapore.Google Scholar
  54. [54]
    Z. Michalewicz and D.B. Fogel (2000) How to Solve it: Modern Heuristics. Springer-Verlag, Berlin.Google Scholar
  55. [55]
    N. Mladenović and P. Hansen (1997) Variable neighborhood search. Computers & Operations Research, 24, 1097–1100.MathSciNetGoogle Scholar
  56. [56]
    H. Mühlenbein (1991) Evolution in time and space—the parallel genetic algorithm. In: Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo. pp. 316–337.Google Scholar
  57. [57]
    M. Nawaz, E. Enscore Jr. and I. Ham (1983) A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. OMEGA, 11(1), 91–95.CrossRefGoogle Scholar
  58. [58]
    G.R. Schreiber and O.C. Martin (1999) Cut size statistics of graph bisection heuristics. SIAM Journal on Optimization, 10(1), 231–251.CrossRefMathSciNetGoogle Scholar
  59. [59]
    M. Singer and M. Pinedo (1997) A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. IIE Scheduling and Logistics, 30, 109–118.Google Scholar
  60. [60]
    T. Stützle (1998). Applying iterated local search to the permutation flow shop problem. Technical Report AIDA-98-04, FG Intellektik, TU Darmstadt, August.Google Scholar
  61. [61]
    T. Stützle (1998) Local Search Algorithms for Combinatorial Problems—Analysis, Improvements, and New Applications. PhD thesis, Darmstadt University of Technology, Department of Computer Science.Google Scholar
  62. [62]
    T. Stützle, A. Grim, S. Linke and M. Rüttger (2000) A comparison of nature inspired heuristics on the traveling salesman problem. In: Deb et al. (eds.), Proceedings of PPSN-VI, volume 1917 of LNCS. Springer Verlag, Berlin, pp. 661–670.Google Scholar
  63. [63]
    T. Stützle and H.H. Hoos (2000) Analyzing the run-time behaviour of iterated local search for the TSP. Technical Report IRIDIA/2000-01, IRIDIA, Université Libre deBruxelles. Available at http://www.intellektik.informatik.tu-darmstadt.de/~tom/pub.html.
  64. [64]
    E.D. Taillard (1995) Comparison of iterative searches for the quadratic assignment problem. Location Science, 3, 87–105.CrossRefzbMATHGoogle Scholar
  65. [65]
    R.J.M. Vaessens, E.H.L. Aarts and J.K. Lenstra (1996) Job shop scheduling by local search. INFORMS Journal on Computing, 8, 302–317.Google Scholar
  66. [66]
    C. Voudouris and E. Tsang (1995) Guided Local Search. Technical Report Technical Report CSM-247, Department of Computer Science, University of Essex.Google Scholar
  67. [67]
    Y. Yang, S. Kreipl and M. Pinedo (2000) Heuristics for minimizing total weighted tardiness in flexible flow shops. Journal of Scheduling, 3(2), 89–108.CrossRefMathSciNetGoogle Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Helena R. Lourenço
    • 1
  • Olivier C. Martin
    • 2
  • Thomas Stützle
    • 3
  1. 1.Universitat Pompeu FabraBarcelonaSpain
  2. 2.Université Paris-SudOrsayFrance
  3. 3.Darmstadt University of TechnologyDarmstadtGermany

Personalised recommendations