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International Conference on Machine Learning and Data Mining in Pattern Recognition

MLDM 2018: Machine Learning and Data Mining in Pattern Recognition pp 126–137Cite as

Optimizing Support Vector Regression with Swarm Intelligence for Estimating the Concrete Compression Strength

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10934))

Abstract

Estimating the compression strength of concrete is a complex problem which has been studied by various researchers. Support Vector Regression (SVR) is a technique that has been shown to be adequate for estimation through input examples. In this paper, we assess three swarm algorithms, Fish School Search (FSS), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) aiming to optimize the SVR parameter. The results show that both all swarm-based algorithms far outperformed the original SVR in the concrete compression strength estimation task and the FSS and ABC obtained better results than PSO for this particular problem.

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References

  1. Montgomery, D.C.: Introduction to Statistical Quality Control. Wiley, Hoboken (2007)

    MATH  Google Scholar 

  2. Huang, H.H., Huang, S.Y.: Nonlinear regression analysis. In: International Encyclopedia of Education, pp. 339–346 (2010)

    Chapter  Google Scholar 

  3. Basak, D., Pal, S., Patranabis, D.C.: Support vector regression. Neural Inf. Process.-Lett. Rev. 11(10), 203–224 (2007)

    Google Scholar 

  4. Kennedy, J.: Particle swarm optimization. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, Boston (2013). https://doi.org/10.1007/978-1-4419-1153-7_200581

    Chapter  Google Scholar 

  5. Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

    Google Scholar 

  6. Bastos Filho, C.J., de Lima Neto, F.B., Lins, A.J., Nascimento, A.I., Lima, M.P.: Fish school search. In: Chiong, R. (ed.) Nature-Inspired Algorithms for Optimisation, vol. 193, pp. 261–277. Springer, Berlin (2009). https://doi.org/10.1007/978-3-642-00267-0_9

    Chapter  Google Scholar 

  7. Yeh, I.C.: Modeling of strength of high-performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)

    Article  Google Scholar 

  8. Almeida Filho, F., Barragán, B.E., Casas, J., El Debs, A.: Hardened properties of self-compacting concrete—a statistical approach. Constr. Build. Mater. 24(9), 1608–1615 (2010)

    Article  Google Scholar 

  9. Aslani, F., Nejadi, S.: Mechanical properties of conventional and self-compacting concrete: an analytical study. Constr. Build. Mater. 36, 330–347 (2012)

    Article  Google Scholar 

  10. de A. Fagundes, R.A., de Souza, R.M.C.R.: Software defect estimation using support vector regression. In: Proceedings of the 22nd International Conference on Software Engineering & Knowledge Engineering (SEKE 2010), Redwood City, San Francisco Bay, CA, USA, 1–3 July 2010, pp. 265–268 (2010)

    Google Scholar 

  11. Taranu, Z.E., Gregory-Eaves, I., Steele, R.J., Beaulieu, M., Legendre, P.: Predicting microcystin concentrations in lakes and reservoirs at a continental scale: a new framework for modelling an important health risk factor. Glob. Ecol. Biogeogr. 26(6), 625–637 (2017)

    Article  Google Scholar 

  12. Soleimani, H., Kannan, G.: A hybrid particle swarm optimization and genetic algorithm for closed-loop supply chain network design in large-scale networks. Appl. Math. Model. 39(14), 3990–4012 (2015)

    Article  MathSciNet  Google Scholar 

  13. Zhao, W., Tao, T., Zio, E., Wang, W.: A novel hybrid method of parameters tuning in support vector regression for reliability prediction: particle swarm optimization combined with analytical selection. IEEE Trans. Reliab. 65(3), 1393–1405 (2016)

    Article  Google Scholar 

  14. Serapião, A.B., Corrêa, G.S., Goncalves, F.B., Carvalho, V.O.: Combining k-means and k-harmonic with fish school search algorithm for data clustering task on graphics processing units. Appl. Soft Comput. 41, 290–304 (2016)

    Article  Google Scholar 

  15. Karaboga, D., Ozturk, C., Karaboga, N., Gorkemli, B.: Artificial bee colony programming for symbolic regression. Inf. Sci. 209, 1–15 (2012)

    Article  Google Scholar 

  16. Rosenblatt, M.: Remarks on some nonparametric estimates of a density function. In: The Annals of Mathematical Statistics, pp. 832–837 (1956)

    Article  MathSciNet  Google Scholar 

  17. Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6(1), 58–73 (2002)

    Article  Google Scholar 

  18. Barnston, A.G.: Correspondence among the correlation, RMSE, and Heidke forecast verification measures; refinement of the Heidke score. Weather Forecast. 7(4), 699–709 (1992)

    Article  Google Scholar 

  19. Wilcoxon, F.: Individual comparisons by ranking methods. Biom. Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

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Authors and Affiliations

  1. University of Pernambuco (UPE), Recife, Pernambuco, Brazil

    Manoel Alves de Almeida Neto, Roberta de Andrade de A. Fagundes & Carmelo J. A. Bastos-Filho

Authors
  1. Manoel Alves de Almeida Neto
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  2. Roberta de Andrade de A. Fagundes
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  3. Carmelo J. A. Bastos-Filho
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Corresponding authors

Correspondence to Manoel Alves de Almeida Neto , Roberta de Andrade de A. Fagundes or Carmelo J. A. Bastos-Filho .

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Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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de Almeida Neto, M.A., de A. Fagundes, R.d.A., Bastos-Filho, C.J.A. (2018). Optimizing Support Vector Regression with Swarm Intelligence for Estimating the Concrete Compression Strength. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10934. Springer, Cham. https://doi.org/10.1007/978-3-319-96136-1_11

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  • DOI: https://doi.org/10.1007/978-3-319-96136-1_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96135-4

  • Online ISBN: 978-3-319-96136-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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