Guided Local Search

  • Christos Voudouris
  • Edward P. K. Tsang
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 57)


The combinatorial explosion problem prevents complete algorithms from solving many real-life combinatorial optimization problems. In many situations, heuristic search methods are needed. This chapter describes the principles of Guided Local Search (GLS) and Fast Local Search (FLS) and surveys their applications. GLS is a penalty-based meta-heuristic algorithm that sits on top of other local search algorithms, with the aim to improve their efficiency and robustness. FLS is a way of reducing the size of the neighborhood so as to improve the efficiency of local search. The chapter also provides guidance for implementing and using GLS and FLS. Four problems, representative of general application categories, are examined with detailed information provided on how to build a GLS-based method in each case.


Heuristic Search Meta-Heuristics Penalty-based Methods Guided Local Search Tabu Search Constraint Satisfaction 


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  1. Anderson, C.A., Fraughnaugh, K., Parker, M. and Ryan, J. (1993) Path assignment for call routing: An application of tabu search. Annals of Operations Research, 41, 301–312.CrossRefGoogle Scholar
  2. Azarmi, N. and Abdul-Hameed, W. (1995) Workforce scheduling with constraint logic programming. BT Technology Journal, 13(1), 81–94.Google Scholar
  3. Backer, B.D., Furnon, V., Shaw, P., Kilby, P. and Prosser, P. (2000) Solving vehicle routing problems using constraint programming and metaheuristics. Journal of Heuristics, 6(4), 501–523.CrossRefGoogle Scholar
  4. Bentley, J.J. (1992) Fast algorithms for geometric traveling salesman problems. ORSA Journal on Computing, 4, 387–111.zbMATHMathSciNetGoogle Scholar
  5. Bouju, A., Boyce, J.F., Dimitropoulos, C.H.D., vom Scheidt, G. and Taylor, J.G. (1995) Intelligent search for the radio link frequency assignment problem. Proceedings of the International Conference on Digital Signal Processing, Cyprus.Google Scholar
  6. Chalmers, A.G. (1994) A Minimum Path Parallel Processing Environment. Research Monographs in Computer Science, Alpha Books.Google Scholar
  7. Choi, K.M.F., Lee, J.H.M. and Stuckey, P.J. A Lagrangian reconstruction of GENET. Artificial Intelligence (to appear).Google Scholar
  8. Chu, P. and Beasley, J.E. (1997) A genetic algorithm for the generalized assignment problem. Computers and Operations Research, 24, 17–23.CrossRefMathSciNetGoogle Scholar
  9. Congram, R.K. and Potts, C.N., (1999) Dynasearch Algorithms for the Traveling Salesman Problem. Presentation at the Travelling Salesman Workshop, CORMSIS, University of Southampton.Google Scholar
  10. Croes, A. (1958) A method for solving traveling-salesman problems. Operations Research, 5, 791–812.MathSciNetGoogle Scholar
  11. Davenport, A., Tsang, E.P.K., Wang, C.J. and Zhu, K. (1994) GENET: a connectionist architecture for solving constraint satisfaction problems by iterative improvement. Proceedings 12th National Conference for Artificial Intelligence (AAAI), pp. 325–330.Google Scholar
  12. Faroe, O., Pisinger, D. and Zachariasen, M (1999) Guided local search for the three-dimensional bin packing problem. Tech. Rep. 99-13, Department of Computer Science, University of Copenhagen.Google Scholar
  13. Freisleben, B. and Merz, P. (1996) A genetic local search algorithm for solving symmetric and asymmetric travelling salesman problems. Proceedings of the 1996 IEEE International Conference on Evolutionary Computation, IEEE Press, pp. 616–621.Google Scholar
  14. Gent, I.P., van Maaren, H. and Walsh, T. (2000) SAT2000, Highlights of satisfiability research in the year 2000. Frontiers in Artificial Intelligence and Applications, IOS Press.Google Scholar
  15. Glover, F. and Laguna, M. (1997) Tabu Search. Kluwer Academic Publishers, Boston.Google Scholar
  16. Goldberg, D.E. (1989) Genetic algorithms in search, optimization, and machine learning, Reading, MA, Addison-Wesley Pub. Co., Inc.Google Scholar
  17. Hansen, P. and Mladenovic, N. (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, Boston, pp. 433–458.Google Scholar
  18. Hao J.-K., Dorne, R. and Galinier, P. (1998) Tabu search for frequency assignment in mobile radio Networks. Journal of Heuristics, 4(1), 47–62.CrossRefGoogle Scholar
  19. Holland, J.H. (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI.Google Scholar
  20. Holstein, D. and Moscato, P. (1999) Memetic algorithms using guided local search: a case study. In: D. Corne, F. Glover, and M. Dorigo (eds.), New Ideas in Optimisation McGraw-Hill, London, pp. 234–244.Google Scholar
  21. Johnson, D. (1990) Local optimization and the traveling salesman problem. Proceedings of the 17th Colloquium on Automata Languages and Programming, Lecture Notes in Computer Science 443, Springer-Verlag, pp. 446–461.Google Scholar
  22. Jose, R. and Boyce, J. (1997) Appication of connectionist local search to line management rail traffic control. Proceedings of International Conf. on Practical Applications of Constraint Technology (PACT’97), London.Google Scholar
  23. Kilby, P., Prosser, P. and Shaw, P. (1999) Guided local search for the vehicle routing problem with time windows. In: S. Voss, S. Martello, I.H. Osman and C. Roucairol (eds.), Meta-Heuristics: Advances and Trends in Local Search Paradigmsfor Optimization. Kluwer Academic Publishers, pp. 473–486.Google Scholar
  24. Kilby, P., Prosser, P. and Shaw, P. (2000) A comparison of traditional and constraint-based heuristic methods on vehicle routing problems with side constraints. Constraints, 5(4), 389–114.CrossRefMathSciNetGoogle Scholar
  25. Knox, J. (1994) Tabu search performance on the symmetric traveling salesman problem. Computers Operations Research, 21(8), 867–876.zbMATHGoogle Scholar
  26. Koopman, B.O. (1957) The theory of search, part III, the optimum distribution of search effort. Operations Research, 5, 613–626.MathSciNetGoogle Scholar
  27. Lau, T.L. and Tsang, E.P.K. (1997) Solving the processor configuration problem with a mutation-based genetic algorithm. International Journal on Artificial Intelligence Tools (IJAIT), 6(4), 567–585.Google Scholar
  28. Lau, T.L. and Tsang, E.P.K. (1998) The guided genetic algorithm and its application to the general assignment problem. IEEE 10th International Conference on Tools with Artificial Intelligence (ICTAI’98), Taiwan, pp. 336–343.Google Scholar
  29. Lau, T.L. and Tsang, E.P.K. (1998) Guided genetic algorithm and its application to the radio link frequency allocation problem. Proceedings of NATO symposium on Frequency Assignment, Sharing and Conservation in Systems (AEROSPACE), AGARD, RTO-MP-13, Paper No. 14b.Google Scholar
  30. Lau, T.L. (1999) Guided Genetic Algorithm. PhD Thesis, Department of Computer Science, University of Essex, Colchester, UK.Google Scholar
  31. Lee, J.H.M. and Tam, V.W.L. (1995) A framework for integrating artificial neural networks and logic programming. International Journal on Artificial Intelligence Tools, 4, 3–32.CrossRefGoogle Scholar
  32. Lin, S. (1965) Computer Solutions of the Traveling-Salesman Problem. Bell Systems Technical Journal, 44, 2245–2269.zbMATHMathSciNetGoogle Scholar
  33. Lin, S. and Kernighan, B.W. (1973) An effective heuristic algorithm for the traveling salesman problem. Operations Research, 21, 498–516.MathSciNetGoogle Scholar
  34. Martin, O., and Otto, S.W. (1966) Combining simulated annealing with local search heuristics. In: G. Laporte and I.H. Osman (eds.), Metaheuristics in Combinatorial Optimization, Annals of Operations Research, Vol. 63.Google Scholar
  35. Mills, P. and Tsang, E.P.K. (2000) Guided local search for solving SAT and weighted MAX-SAT problems. Journal of Automated Reasoning, 24, 205–223.CrossRefMathSciNetGoogle Scholar
  36. Mills, P. and Tsang, E.P.K. (2000) An empirical study of extended guided local search on the quadratic assignment problem. Manuscript, submitted to Nareyek, A. (ed.), Post-Proceedings, ECAI-2000 Workshop on Local Search for Planning and Scheduling, Springer LNCS/LNAI book series.Google Scholar
  37. Minton S., Johnston, M.D., Philips A.B. and Laird, P. (1992) Minimizing conflicts: a heuristic repair method for constraint satisfaction and scheduling problems. Artificial Intelligence 58(1–3) (Special Volume on Constraint Based Reasoning), 161–205.MathSciNetGoogle Scholar
  38. Murphey, R.A., Pardalos, P.M. and Resende, M.G.C. (1999) Frequency assignment problems. In: D.-Z Du and P. Pardalos (eds.), Handbook of Combinatorial Optimization, Vol. 4, Kluwer Academic Publishers.Google Scholar
  39. Padron, V. and Balaguer, C. (2000) New Methodology to solve the RPP by means of Isolated Edge. In: A. Tuson (ed.), 2000 Cambridge Conference Tutorial Papers, Young OR 11, UK Operational Research Society.Google Scholar
  40. Pesant, G. and Gendreau, M. (1999) A constraint programming framework for local search methods. Journal of Heuristics, 5(3), 255–279.CrossRefGoogle Scholar
  41. R.E. Burkard, R.E., Karisch, S.E. and Rendl F. (1997) QAPLIB—a quadratic assignment problem library. Journal of Global Optimization, 10, 391–403.CrossRefMathSciNetGoogle Scholar
  42. Reinelt, G. (1991) A traveling salesman problem library. ORSA Journal on Computing, 3, 376–384.zbMATHGoogle Scholar
  43. Reinelt, G. (1995) The traveling salesman: computational solutions for TSP applications. Lecture Notes in Computer Science, Vol. 840, Springer-Verlag.Google Scholar
  44. Resende, M.G.C. and Feo, T.A. (1996) A GRASP for satisfiability. In: D.S. Johnson and M. A. Trick (eds.), Cliques, coloring, and satisfiability: Second DIMACS Implementation Challenge. DIMACS Series on Discrete Mathematics and Theoretical Computer Science, American Mathematical Society, Vol. 26, pp. 499–520.Google Scholar
  45. Selman, B., Levesque, H. J. and Mitchell, D. G. (1992) A new method for solving hard satisfiability problems. Proceedings of AAAI-92, 440–446.Google Scholar
  46. Selman, B. and Kautz, H. (1993) Domain-independent extensions to GSAT: solving large structured satisfiability problems. Proceedings of 13th International Joint Conference on AI, 290–295.Google Scholar
  47. Shang, Y. and Wah, B.W. (1998) A discrete lagrangian-based global-search method for solving satisfiability problems. Journal of Global Optimization, 12(1), 61–99.CrossRefMathSciNetGoogle Scholar
  48. Simon, H. U. (1989) Approximation algorithms for channel assignment in cellular radio networks. Proceedings 7th International Symposium on Fundamentals of Computation Theory, Lecture Notes in Computer Science, Vol. 380. Springer-Verlag, pp. 405–416.zbMATHGoogle Scholar
  49. Stone, L.D. (1983) The process of search planning: current approaches and continuing problems. Operations Research, 31, 207–233.Google Scholar
  50. Stuckey, P. and Tam, V. (1998) Semantics for using stochastic constraint solvers in constraint logic programming. Journal of Functional and Logic Programming, 2.Google Scholar
  51. Taillard, E. (1991) Robust taboo search for the QAP. Parallel Computing 17, 443–455.CrossRefMathSciNetGoogle Scholar
  52. Taillard, E. (1995) Comparison of iterative searches for the quadratic assignment problem. Location Science, 3, 87–105.CrossRefzbMATHGoogle Scholar
  53. Tiourine, S., Hurkins, C. and Lenstra, J.K. (1995) An overview of algorithmic approaches to frequency assignment problems. EUCLID CALMA Project Overview Report, Delft University of Technology, The Netherlands.Google Scholar
  54. Tsang, E.P.K. and Wang, C. J. (1992) A generic neural network approach for constraint satisfaction problems. In: J.G. Taylor (ed.), Neural Network Applications, Springer-Verlag, pp. 12–22.Google Scholar
  55. Tsang, E.P.K. and Voudouris, C. (1997) Fast local search and guided local search and their application to British Telecom’s workforce scheduling problem. Operations Research Letters, 20(3), 119–127.CrossRefGoogle Scholar
  56. Tsang, E.P.K., Wang, C.J., Davenport, A., Voudouris, C. and Lau, T.L. (1999) A family of stochastic methods for constraint satisfaction and optimisation. Proceedings of the First International Conference on The Practical Application of Constraint Technologies and Logic Programming (PACLP), London, pp. 359–383.Google Scholar
  57. Voudouris, C. and Tsang, E.P.K. (1996) Partial constraint satisfaction problems and guided local search. Proceedings ofPACT’96, London, pp. 337–356.Google Scholar
  58. Voudouris, C. (1997) Guided Local Searchfor Combinatorial Optimisation Problems. PhD Thesis, Department of Computer Science, University of Essex, Colchester, UK.Google Scholar
  59. Voudouris, C. (1998) Guided Local Search—An illustrative example in function optimisation. BT Technology Journal, 16(3), 46–50.Google Scholar
  60. Voudouris, C. and Tsang, E. (1998) Solving the Radio Link Frequency Assignment Problems using Guided Local Search. Proceedings of NATO symposium on Frequency Assignment, Sharing and Conservation in Systems (AEROSPACE), AGARD, RTO-MP-13, Paper No. 14a.Google Scholar
  61. Voudouris, C. and Tsang, E.P.K. (1999) Guided Local Search and its application to the Travelling Salesman Problem. European Journal of Operational Research, 113(2), 469–499.CrossRefGoogle Scholar
  62. Wang, C.J. and Tsang, E.P.K. (1991) Solving constraint satisfaction problems using neural-networks. Proceedings of the IEE Second International Conference on Artificial Neural Networks, pp. 295–299.Google Scholar
  63. Wang, C.J. and Tsang, E.P.K. (1994) A cascadable VLSI design for GENET. In: J.G. Delgado-Frias and W.R. Moore (eds.), VLSI for Neural Networks and Artificial Intelligence. Plenum Press, New York, pp. 187–196.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  • Christos Voudouris
    • 1
  • Edward P. K. Tsang
    • 2
  1. 1.Research Department, BTexact TechnologiesMarthlesham HeathIpswichUK
  2. 2.Department of Computer ScienceUniveristy of EssexColchesterUK

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