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Advances in Clustering Search

  • Tarcisio Souza Costa
  • Alexandre César Muniz de Oliveira
  • Luiz Antonio Nogueira Lorena
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

Abstract

The Clustering Search (*CS) has been proposed as a generic way of combining search metaheuristics with clustering to detect promising search areas before applying local search procedures. The clustering process may keep representative solutions associated to different search subspaces. Although, recent applications have reached success in combinatorial optimisation problems, nothing new has arisen concerning diversification issues when population metaheuristics, as evolutionary algorithms, are being employed. In this work, recent advances in the *CS are commented and new features are proposed, including, the possibility of keeping population diversified for more generations.

Keywords

Search Area Variable Neighborhood Search Promising Area Continuous Optimisation Local Search Procedure 
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.

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References

  1. 1.
    Oliveira, A.C.M., Lorena, L.A.N.: Detecting promising areas by evolutionary clustering search. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 385–394. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Oliveira, A.C.M., Lorena, L.A.N.: Hybrid evolutionary algorithms and clustering search. In: Grosan, C., Abraham, A., Ishibuchi, H. (eds.) Hybrid Evolutionary Systems. SCI, vol. 75, pp. 81–102 (2007)Google Scholar
  3. 3.
    Chaves, A.A., Lorena, L.A.N.: Hybrid algorithms with detection of promising areas for the prize collecting travelling salesman problem. In: HIS 2005: Proceedings of the Fifth International Conference on Hybrid Intelligent Systems, pp. 49–54. IEEE Computer Society, Washington (2005)CrossRefGoogle Scholar
  4. 4.
    Resende, M.G.C.: Greedy randomized adaptive search procedures (grasp). Journal of Global Optimization 6, 109–133 (1999)MathSciNetGoogle Scholar
  5. 5.
    Hansen, P., Mladenovic, N.: Variable neighborhood search. Computers and Operations Research 24, 1097–1100 (1997)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Biajoli, F.L., Lorena, L.A.N.: Clustering Search Approach for the Traveling Tournament Problem. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 83–93. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Chaves, A.A., Correa, F.A., Lorena, L.A.N.: Clustering Search Heuristic for the Capacitated p-median Problem. Springer Advances in Software Computing Series 44, 136–143 (2007)Google Scholar
  8. 8.
    Oliveira, A.C.M., Lorena, L.A.N.: Pattern Sequencing Problems by Clustering Search. In: Sichman, J.S., Coelho, H., Rezende, S.O. (eds.) IBERAMIA 2006 and SBIA 2006. LNCS (LNAI), vol. 4140, pp. 218–227. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  9. 9.
    Glover, F., Laguna, M.: Fundamentals of scatter search and path relinking. Control and Cybernetics 29(3), 653–684 (2000)zbMATHMathSciNetGoogle Scholar
  10. 10.
    Filho, G.R., Nagano, M.S., Lorena, L.A.N.: Evolutionary clustering search for flowtime minimization in permutation flow shop. In: Hybrid Metaheuristics, pp. 69–81 (2007)Google Scholar
  11. 11.
    Hooke, R., Jeeves, T.A.: “Direct search” solution of numerical and statistical problems. Journal of the ACM 8(2), 212–229 (1961)zbMATHCrossRefGoogle Scholar
  12. 12.
    Digalakis, J., Margaritis, K.: An experimental study of benchmarking functions for Genetic Algorithms. IEEE Systems Transactions, 3810–3815 (2000)Google Scholar
  13. 13.
    Oliveira, A.: Algoritmos evolutivos híbridos com detecção de regiões promissoras em espaços de busca contínuos e discretos. PhD Thesis. INPE (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Tarcisio Souza Costa
    • 1
  • Alexandre César Muniz de Oliveira
    • 1
  • Luiz Antonio Nogueira Lorena
    • 2
  1. 1.Universidade Federal do MaranhãoSão LuísBrasil
  2. 2.Instituto Nacional de Pesquisas Espaciais 

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