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Journal of Heuristics

, Volume 25, Issue 4–5, pp 539–564 | Cite as

Intensification, learning and diversification in a hybrid metaheuristic: an efficient unification

  • Vinícius R. Máximo
  • Mariá C. V. NascimentoEmail author
Article
  • 170 Downloads

Abstract

Hybrid heuristic methods have lately been pointed out as an efficient approach to combinatorial optimization problems. The main reason behind this is that, by combining components from different metaheuristics, it is possible to explore solutions (which would be unreachable without hybridization) in the search space. In particular, evolutionary algorithms may get trapped into local optimum solutions due to the insufficient diversity of the solutions influencing the search process. This paper presents a hybridization of the recently proposed metaheuristic—intelligent-guided adaptive search (IGAS)—with the well-known path-relinking algorithm to solve large scale instances of the maximum covering location problem. Moreover, it proposes a slight change in IGAS that was tested through computational experiments and has shown improvement in its computational cost. Computational experiments also attested that the hybridized IGAS outperforms the results found in the literature.

Keywords

Intelligent-guided adaptive search Path-relinking Maximum covering location problem Large scale 

Notes

Acknowledgements

The authors are grateful to Fundação de Amparo á Pesquisa do Estado de São Paulo (FAPESP) (Grant No. 15/21660-4) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Grant No. 308708/2015-6, 448614/2014-6) for their financial support. Research carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI) funded by FAPESP (Grant No. 2013/07375-0).

References

  1. Aiex, R.M., Resende, M.G.C., Ribeiro, C.C.: TTT plots: a perl program to create time-to-target plots. Optim. Lett. 1, 355–366 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  2. Church, R., ReVelle, C.: The maximal covering location problem. Pap. Reg. Sci. Assoc. 32, 101–118 (1974)CrossRefGoogle Scholar
  3. CPLEX: IBM ILOG CPLEX Optimization Studio CPLEX User’s Manual Version 12 Release 6, IBM (2014)Google Scholar
  4. Dolan, E.D., Moré, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91, 201–213 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  5. Feo, T.A., Resende, M.G.C.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8, 67–71 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  6. Fritzke, B.: Growing cell structures—a self-organizing network for unsupervised and supervised learning. Neural Netw. 7, 1441–1460 (1994)CrossRefGoogle Scholar
  7. Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.): Advances in Neural Information Processing Systems 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  8. Galvão, R.D., ReVelle, C.: A Lagrangean heuristic for the maximal covering location problem. Eur. J. Oper. Res. 88, 114–123 (1996)CrossRefzbMATHGoogle Scholar
  9. Galvão, R.D., Espejo, L.G.A., Boffey, B.: A comparison of Lagrangean and surrogate relaxations for the maximal covering location problem. Eur. J. Oper. Res. 124, 377–389 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  10. Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co., New York (1979)zbMATHGoogle Scholar
  11. Glover, F.: Tabu search and adaptive memory programing—advances, applications and challenges. In: Barr, R.S., Helgason, R.V., Kennington, J.L. (eds.) Interfaces in Computer Science and Operations Research, pp. 1–75. Kluwer, Dordrecht (1996)Google Scholar
  12. Glover, F.: A template for scatter search and path relinking. In: Hao, J.-K., Lutton, E., Ronald, E., Schoenauer, M., Snyers, D. (eds.) Artificial Evolution, Volume 1363 of Lecture Notes in Computer Science, pp. 1–51. Springer, Berlin (1998)Google Scholar
  13. Glover, F.: Scatter search and path relinking. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 297–316. McGraw Hill (1999)Google Scholar
  14. Heinke, D., Hamker, F.H.: Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy artmap. IEEE Trans. Neural Netw. 9, 1279–1291 (1998)CrossRefGoogle Scholar
  15. Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Int. Res. 36, 267–306 (2009)zbMATHGoogle Scholar
  16. Jia, H., Ordóñez, F., Dessouky, M.M.: Solution approaches for facility location of medical supplies for large-scale emergencies. Comput. Ind. Eng. 52, 257–276 (2007)CrossRefGoogle Scholar
  17. Karasakal, O., Karasakal, E.K.: A maximal covering location model in the presence of partial coverage. Comput. Oper. Res. 31, 1515–1526 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  18. Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43, 59–69 (1982)CrossRefzbMATHGoogle Scholar
  19. Lorena, L.A.N., Pereira, M.A.: A Lagrangean/surrogate heuristic for the maximal covering location problem using Hillman’s edition. Int. J. Ind. Eng. 9, 57–67 (2002)Google Scholar
  20. Martinetz, T.: Competitive Hebbian learning rule forms perfectly topology preserving maps. In: Gielen, S., Kappen, B. (eds.) Proceedings of the International Conference on Artificial Neural Networks (ICANN-93), pp. 427–434. Springer, Amsterdam (1993)Google Scholar
  21. Martinetz, T., Schulten, K.: A “neural-gas” network learns topologies. In: Kohonen, T., Makisara, K., Simula, O., Kangas, J. (eds.) Artificial Neural Networks, pp. 397–402. Elsevier Science Publishers B. V., North-Holland (1991)Google Scholar
  22. Máximo, V.R., Nascimento, M.C.V., Carvalho, A.C.P.L.F.: Intelligent-guided adaptive search for the maximum covering location problem. Comput. Oper. Res. 78, 129–137 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  23. Pessoa, L.S., Resende, M.G.C., Ribeiro, C.C.: A hybrid Lagrangean heuristic with GRASP and path-relinking for set k-covering. Comput. Oper. Res. 40, 3132–3146 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  24. Resende, M.G.C.: Computing approximate solutions of the maximum covering problem with GRASP. J. Heurist. 4, 161–177 (1998)CrossRefzbMATHGoogle Scholar
  25. Resende, M.G.C., Ribeiro, C.C.: GRASP with path-relinking: recent advances and applications. In: Ibaraki, T., Nonobe, K., Yagiura, M. (eds.) Metaheuristics: Progress as Real Problem Solvers, Volume 32 of Operations Research/Computer Science Interfaces Series, pp. 29–63. Springer, New York (2005)CrossRefGoogle Scholar
  26. Resende, M.G.C., Werneck, R.F.: A hybrid multistart heuristic for the uncapacitated facility location problem. Eur. J. Oper. Res. 174, 54–68 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  27. ReVelle, C., Scholssberg, M., Williams, J.: Solving the maximal covering location problem with heuristic concentration. Comput. Oper. Res. 35, 427–435 (2008). Part Special Issue: Location Modeling Dedicated to the memory of Charles S. ReVelleMathSciNetCrossRefzbMATHGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto de Ciência e TecnologiaUniversidade Federal de São Paulo (UNIFESP)São José dos CamposBrazil

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