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Making a state-of-the-art heuristic faster with data mining

  • Data Mining and Analytics
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Abstract

Hybrid metaheuristics—developed based on the combination of metaheuristics with concepts and techniques from other research areas—represent an important subject in combinatorial optimization research. Data mining techniques have been coupled with metaheuristics in order to obtain patterns of suboptimal solutions, which are used to guide the search for better-cost solutions. In this paper, we incorporate a data mining procedure into a state-of-the-art heuristic for a specific problem in order to give evidences that, when a technique is able to reach an optimal solution, or a near-optimal solution with little chance of improvements, the mined patterns could be used to guide the search for the optimal or near optimal solution in less computational time. We developed a data mining hybrid version of a previously proposed and state-of-the-art multistart heuristic for the classical \(p\)-median problem. Computational experiments, conducted on a set of instances from the literature, showed that the new version of the heuristic was able to reach optimal and near-optimal solutions, on average, 27.32 % faster than the original strategy.

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References

  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the very large data bases conference (pp. 487–499).

  • Aiex, R., Resende, M. G. C., & Ribeiro, C. (2007). TTT plots: A perl program to create time-to-target plots. Optimization Letters, 4, 355–366.

    Article  Google Scholar 

  • Aloise, D., & Ribeiro, C. C. (2011). Adaptive memory in multistart heuristics for multicommodity network design. Journal of Heuristics, 17, 153–179.

    Article  Google Scholar 

  • Beasley, J. E. (1985). A note on solving large p-median problems. European Journal of Operational Research, 21, 270–273.

    Article  Google Scholar 

  • Berger, D., Gendron, B., Potvin, J.-Y., Raghavan, S., & Soriano, P. (2000). Tabu search for a network loading problem with multiple facilities. Journal of Heuristics, 6, 253–267.

    Article  Google Scholar 

  • Cornuéjols, G., Fisher, M. L., & Nemhauser, G. L. (1977). Location of bank accounts to optimize float: An analytical study of exact and approximate algorithms. Management Science, 23, 789–810.

    Article  Google Scholar 

  • Festa, P., & Resende, M. G. C. (2009a). An annotated bibliography of GRASP—Part I: Algorithms. International Transactions in Operational Research, 16, 1–24.

    Article  Google Scholar 

  • Festa, P., & Resende, M. G. C. (2009b). An annotated bibliography of GRASP—Part II: Applications. International Transactions in Operational Research, 16, 131–172.

    Article  Google Scholar 

  • Fleurent, C., & Glover, F. (1999). Improved constructive multistart strategies for the quadratic assignment problem using adaptive memory. INFORMS Journal on Computing, 2, 198–204.

    Article  Google Scholar 

  • Gendreau, M., & Potvin, J.-Y. (2010). Handbook of metaheuristics. New York: Springer.

  • Glover, F. (1992). New ejection chain and alternating path methods for traveling salesman problems, computer science and operations research. In O. Balci, R. Sharda, & S. Zenios (Eds.), New developments and their interfaces (pp. 449–509). Pergamon Press.

  • Glover, F. (2000). Multi-start and strategic oscillation methods—Principles to exploit adaptive memory, computing tools for modeling, optimization and simulation: Interfaces in computer science and operations research. US: Springer.

  • Glover, F., Laguna, M., & Martí, R. (1977). Fundamentals of scatter search and path-relinking. Control and Cybernetics, 19, 653–684.

    Google Scholar 

  • Glover, F., Laguna, M., & Martí, R. (2003). Scatter search and path relinking: Advances and applications. Handbook of Metaheuristics. US: Springer.

  • Goethals, B., & Zaki, M. J. (2003). Advances in frequent itemset mining implementations: Introduction to FIMI-03. In Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations.

  • Grahne, G., & Zhu, J. (2003). Efficiently using prefix-trees in mining frequent item-sets. In Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations.

  • Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD international conference on management of data (pp. 1–12).

  • Han, J., & Kamber, M. (2011). Data mining: Concepts and techniques (3rd ed.). San Francisco: Morgan Kaufmann Publishers.

  • Hansen, P., & Mladenović, N. (1997). Variable neighborhood search for the p-median. Location Science, 5, 207–226.

    Article  Google Scholar 

  • Hansen, P., Mladenović, N., & Perez-Brito, D. (2001). Variable neighborhood decomposition search. Journal of Heuristics, 7, 335–350.

    Article  Google Scholar 

  • Kariv, O., & Hakimi, L. (1979). An algorithmic approach to network location problems, part II: The p-medians. SIAM Journal of Applied Mathematics, 37, 539–560.

    Article  Google Scholar 

  • Lin, S., & Kernighan, B. W. (1973). An effective heuristic algorithm for the traveling salesman problem. Operations Research, 21, 498–516.

    Article  Google Scholar 

  • Lodi, A., Allemand, K., & Liebling, T. M. (1999). An evolutionary heuristic for quadratic 0–1 programming. European Journal of Operational Research, 119, 662–670.

    Article  Google Scholar 

  • Orlando, S., Palmerimi, P., & Perego, R. (2002). Adaptive and resource-aware mining of frequent sets. In Proceedings of the IEEE international conference on data mining (pp. 338–345).

  • Osman, I., & Laporte, G. (1996). Metaheuristics: A bibliography. Annals of Operations Research, 63, 513–623.

    Article  Google Scholar 

  • Rao, M. R. (1971). Cluster analysis and mathematical programming. Journal of the American Statistical Association, 66, 622–626.

    Article  Google Scholar 

  • Reinelt, G. (1991). TSPLIB: A traveling salesman problem library. ORSA Journal on Computing, 3, 376–384.

    Article  Google Scholar 

  • Resende, M. G. C., & Ribeiro, C. C. (2013). Greedy randomized adaptive search procedures. In E. K. Burke & G. Kendall (Eds.), Search methodologies (2nd ed., pp. 285–310). US: Springer.

  • Resende, M. G. C. & Werneck, R. F. (2003). On the implementation of a swap-based local search procedure for the \(p\)-median problem. In Proceedings of the fifth workshop on algorithm engineering and experiments—ALENEX03 (pp. 119–127).

  • Resende, M. G. C., & Werneck, R. F. (2004). A hybrid heuristic for the p-median problem. Journal of Heuristics, 10, 59–88.

    Article  Google Scholar 

  • Ribeiro, M. H. F., Trindade, V. F., Plastino, A., & Martins, S. L. (2004). Hybridization of GRASP metaheuristic with data mining techniques. In Proceedings of the ECAI workshop on hybrid metaheuristics (pp. 69–78).

  • Ribeiro, M. H. F., Trindade, V. F., Plastino, A., & Martins, S. L. (2006). Hybridization of GRASP metaheuristic with data mining techniques. Journal of Mathematical Modeling and Algorithms, 5, 23–41.

    Article  Google Scholar 

  • Salhi, S. (2006). Heuristic search: The science of tomorrow. In British operational research conference (OR48) Keynote Papers, Operational Research Society (pp. 38–58).

  • Santos, L. F., Ribeiro, M. H. F., Plastino, A., & Martins, S. L. (2005). A hybrid GRASP with data mining for the maximum diversity problem. In Proceedings of the international workshop on hybrid metaheuristics, LNCS, 3636 (pp. 116–127).

  • Santos, L. F., Albuquerque, C. V., Martins, S. L., & Plastino, A. (2006). A hybrid GRASP with data mining for efficient server replication for reliable multicast. In Proceedings of the IEEE GLOBECOM conference.

  • Santos, L. F., Martins, S. L., & Plastino, A. (2008). Applications of the DM-GRASP heuristic: A survey. International Transactions in Operational Research, 15, 387–416.

    Article  Google Scholar 

  • Senne, E. L. F., & Lorena, L. A. N. (2000). Langrangean/Surrogate Heuristics for p-Median Problems. In M. Laguna & J. L. González-Velarde (Eds.), Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research (pp. 115–130). Boston: Kluwer Academic.

  • Taillard, E. D. (2003). Heuristic methods for large centroid clustering problems. Journal of Heuristics, 9, 51–74.

    Article  Google Scholar 

  • Talbi, E. G. (2002). A taxonomy of hybrid metaheuristics. Journal of Heuristics, 8, 541–564.

    Article  Google Scholar 

  • Tansel, B. C., Francis, R. L., & Lowe, T. J. (1983). Location on networks: A survey. Management Science, 29, 482–511.

    Article  Google Scholar 

  • Teitz, M. B., & Bart, P. (1968). Heuristic methods for estimating the generalized vertex median of a weighted graph. Operations Research, 16, 955–961.

    Article  Google Scholar 

  • Vinod, H. D. (1969). Integer programming and the theory of groups. Journal of the American Statistical Association, 64, 506–519.

    Article  Google Scholar 

  • Whitaker, R. (1983). A fast algorithm for the greedy interchange of large-scale clustering and median location problems. INFOR, 21, 95–108.

    Google Scholar 

  • Witten, I. H., & Frank, E. (2011). Data mining: Practical machine learning tools and techniques (3rd ed.). San Francisco: Morgan Kaufmann Publishers.

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Authors and Affiliations

  1. Department of Computer Science, Federal Fluminense University, Niterói, Rio de Janeiro, Brazil

    Daniel Martins, Gabriel M. Vianna, Isabel Rosseti, Simone L. Martins & Alexandre Plastino

Authors
  1. Daniel Martins
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  2. Gabriel M. Vianna
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  3. Isabel Rosseti
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  4. Simone L. Martins
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  5. Alexandre Plastino
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Corresponding author

Correspondence to Alexandre Plastino.

Additional information

This work was supported by CAPES, CNPq and FAPERJ research grants.

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Martins, D., Vianna, G.M., Rosseti, I. et al. Making a state-of-the-art heuristic faster with data mining. Ann Oper Res 263, 141–162 (2018). https://doi.org/10.1007/s10479-014-1693-4

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