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Memetic Computing

, Volume 10, Issue 3, pp 333–350 | Cite as

EB-GLS: an improved guided local search based on the big valley structure

  • Jialong Shi
  • Qingfu Zhang
  • Edward Tsang
Regular Research Paper

Abstract

Local search is a basic building block in memetic algorithms. Guided local search (GLS) can improve the efficiency of local search. By changing the guide function, GLS guides a local search to escape from locally optimal solutions and find better solutions. The key component of GLS is its penalizing mechanism which determines which feature is selected to penalize when the search is trapped in a locally optimal solution. The original GLS penalizing mechanism only makes use of the cost and the current penalty value of each feature. It is well known that many combinatorial optimization problems have a big valley structure, i.e., the better a solution is, the more the chance it is closer to a globally optimal solution. This paper proposes to use big valley structure assumption to improve the GLS penalizing mechanism. An improved GLS algorithm called elite biased GLS (EB-GLS) is proposed. EB-GLS records and maintains an elite solution as an estimate of the globally optimal solutions, and reduces the chance of penalizing the features in this solution. We have systematically tested the proposed algorithm on the symmetric traveling salesman problem. Experimental results show that EB-GLS is significantly better than GLS.

Keywords

Combinatorial optimization Metaheuristics Traveling salesman problem Guided local search Elitism 

Notes

Acknowledgements

The work described in this paper was supported by a grant from ANR/RCC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityU101/16).

References

  1. 1.
    Alhindi A, Zhang Q (2013) MOEA/D with guided local search: some preliminary experimental results. In: Computer science and electronic engineering conference (CEEC), 2013 5th, IEEE, pp 109–114Google Scholar
  2. 2.
    Alsheddy A, Tsang E (2011) Empowerment scheduling for a field workforce. J Sched 14(6):639–654Google Scholar
  3. 3.
    Basharu M, Arana I, Ahriz H (2005) Distributed guided local search for solving binary DisCSPs. In: FLAIRS conference, pp 660–665Google Scholar
  4. 4.
    Bentley JJ (1992) Fast algorithms for geometric traveling salesman problems. ORSA J Comput 4(4):387–411MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Boese KD (1995) Cost versus distance in the traveling salesman problem. UCLA Computer Science Department, Los AngelesGoogle Scholar
  6. 6.
    Dorigo M, Birattari M, Stutzle T (2006) Ant colony optimization. IEEE Comput Intell Mag 1(4):28–39CrossRefGoogle Scholar
  7. 7.
    Eiben ÁE, Van Der Hauw JK, van Hemert JI (1998) Graph coloring with adaptive evolutionary algorithms. J Heuristics 4(1):25–46CrossRefzbMATHGoogle Scholar
  8. 8.
    Hains DR, Whitley LD, Howe AE (2011) Revisiting the big valley search space structure in the TSP. J Oper Res Soc 62(2):305–312CrossRefGoogle Scholar
  9. 9.
    Hasan SK, Sarker R, Essam D, Cornforth D (2009) Memetic algorithms for solving job-shop scheduling problems. Memet Comput 1(1):69–83CrossRefGoogle Scholar
  10. 10.
    Helsgaun K (2000) An effective implementation of the Lin–Kernighan traveling salesman heuristic. Eur J Oper Res 126(1):106–130MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Jadon SS, Bansal JC, Tiwari R, Sharma H (2015) Accelerating artificial bee colony algorithm with adaptive local search. Memet Comput 7(3):215–230CrossRefGoogle Scholar
  12. 12.
    Jones T (1995) Evolutionary algorithms, fitness landscapes and search. Ph.D. Thesis, CiteseerGoogle Scholar
  13. 13.
    Jones T, Forrest S (1995) Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In: Eshelman LJ (ed) Proceedings of the 6th international conference on genetic algorithms. Morgan Kaufmann, San Francisco, CA, pp 184–192Google Scholar
  14. 14.
    Kauffman SA (1993) The origins of order: self-organization and selection in evolution. Oxford University Press, OxfordGoogle Scholar
  15. 15.
    Lau T, Tsang E (2001) Guided genetic algorithm and its application to radio link frequency assignment problems. Constraints 6(4):373–398CrossRefzbMATHGoogle Scholar
  16. 16.
    Lourenço HR, Martin OC, Stützle T (2010) Iterated local search: framework and applications. In: Handbook of metaheuristics, Springer, pp 363–397Google Scholar
  17. 17.
    Marinaki M, Marinakis Y (2015) A hybridization of clonal selection algorithm with iterated local search and variable neighborhood search for the feature selection problem. Memet Comput 7(3):181–201CrossRefGoogle Scholar
  18. 18.
    Merz P, Freisleben B (2000) Fitness landscape analysis and memetic algorithms for the quadratic assignment problem. IEEE Trans Evol Comput 4(4):337–352CrossRefGoogle Scholar
  19. 19.
    Merz P, Freisleben B (2001) Memetic algorithms for the traveling salesman problem. Complex Syst 13(4):297–346MathSciNetzbMATHGoogle Scholar
  20. 20.
    Merz P, Katayama K (2004) Memetic algorithms for the unconstrained binary quadratic programming problem. BioSyst 78(1):99–118CrossRefGoogle Scholar
  21. 21.
    Mester D, Bräysy O (2005) Active guided evolution strategies for large-scale vehicle routing problems with time windows. Comput Oper Res 32(6):1593–1614CrossRefzbMATHGoogle Scholar
  22. 22.
    Mills P, Tsang E, Ford J (2003) Applying an extended guided local search to the quadratic assignment problem. Ann Oper Res 118(1):121–135MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Ochoa G, Veerapen N (2016) Deconstructing the big valley search space hypothesis. In: Evolutionary computation in combinatorial optimization, Springer, pp 58–73Google Scholar
  24. 24.
    Oliveira SM, Hussin MS, Stützle T, Roli A, Dorigo M (2011) A detailed analysis of the population-based ant colony optimization algorithm for the TSP and the QAP. In: Proceedings of the 13th annual conference companion on genetic and evolutionary computation, ACM, pp 13–14Google Scholar
  25. 25.
    Reeves CR (1999) Landscapes, operators and heuristic search. Ann Oper Res 86:473–490MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Reinelt G (1991) TSPLIB—a traveling salesman problem library. ORSA J Comput 3(4):376–384CrossRefzbMATHGoogle Scholar
  27. 27.
    Tao X, Haubrich HJ (2005) A hybrid metaheuristic method for the planning of medium-voltage power distribution systemsGoogle Scholar
  28. 28.
    Tarantilis CD, Zachariadis EE, Kiranoudis CT (2008) A hybrid guided local search for the vehicle-routing problem with intermediate replenishment facilities. INFORMS J Comput 20(1):154–168MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Vansteenwegen P, Souffriau W, Berghe GV, Van Oudheusden D (2009) A guided local search metaheuristic for the team orienteering problem. Eur J Oper Res 196(1):118–127CrossRefzbMATHGoogle Scholar
  30. 30.
    Voudouris C, Tsang E (1999) Guided local search and its application to the traveling salesman problem. Eur J Oper Res 113(2):469–499CrossRefzbMATHGoogle Scholar
  31. 31.
    Voudouris C, Tsang E, Alsheddy A (2010) Guided local search. In: Handbook of metaheuristics, Springer, pp 321–361Google Scholar
  32. 32.
    Zachariadis EE, Tarantilis CD, Kiranoudis CT (2009) A guided tabu search for the vehicle routing problem with two-dimensional loading constraints. Eur J Oper Res 195(3):729–743CrossRefzbMATHGoogle Scholar
  33. 33.
    Zhang W (2004) Configuration landscape analysis and backbone guided local search: part i: satisfiability and maximum satisfiability. Artif Intell 158(1):1–26MathSciNetCrossRefzbMATHGoogle Scholar
  34. 34.
    Zhong Y, Cole MH (2005) A vehicle routing problem with backhauls and time windows: a guided local search solution. Transp Res Part E: Logist Transp Rev 41(2):131–144CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceCity University of Hong KongHong KongHong Kong
  2. 2.Centre for Computational Finance and Economic Agents, School of Computer Science and Electronic EngineeringUniversity of EssexColchesterUK

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