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Towards a High-Performance Implementation of the MCSFilter Optimization Algorithm

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Optimization, Learning Algorithms and Applications (OL2A 2021)

Abstract

Multistart Coordinate Search Filter (MCSFilter) is an optimization method suitable to find all minimizers – both local and global – of a non convex problem, with simple bounds or more generic constraints. Like many other optimization algorithms, it may be used in industrial contexts, where execution time may be critical in order to keep a production process within safe and expected bounds. MCSFilter was first implemented in MATLAB and later in Java (which introduced a significant performance gain). In this work, a comparison is made between these two implementations and a novel one in C that aims at further performance improvements. For the comparison, the problems addressed are bound constraint, with small dimension (between 2 and 10) and multiple local and global solutions. It is possible to conclude that the average time execution for each problem is considerable smaller when using the Java and C implementations, and that the current C implementation, though not yet fully optimized, already exhibits a significant speedup.

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References

  1. Abhishek, K., Leyffer, S., Linderoth, J.: FilMINT: an outer-approximation-based solver for convex mixed-integer nonlinear programs. INFORMS J. Comput. 22(4), 555–567 (2010)

    Article  MathSciNet  Google Scholar 

  2. Abramson, M., Audet, C., Chrissis, J., Walston, J.: Mesh adaptive direct search algorithms for mixed variable optimization. Optim. Lett. 3(1), 35–47 (2009). https://doi.org/10.1007/s11590-008-0089-2

    Article  MathSciNet  MATH  Google Scholar 

  3. Amador, A., Fernandes, F.P., Santos, L.O., Romanenko, A., Rocha, A.M.A.C.: Parameter estimation of the kinetic \(\alpha \)-Pinene isomerization model using the MCSFilter algorithm. In: Gervasi, O., et al. (eds.) ICCSA 2018, Part II. LNCS, vol. 10961, pp. 624–636. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95165-2_44

    Chapter  Google Scholar 

  4. Amador, A., Fernandes, F.P., Santos, L.O., Romanenko, A.: Application of MCSFilter to estimate stiction control valve parameters. In: International Conference of Numerical Analysis and Applied Mathematics, AIP Conference Proceedings, vol. 1863, pp. 270005 (2017)

    Google Scholar 

  5. Belotti, P., Kirches, C., Leyffer, S., Linderoth, J., Mahajan, A.: Mixed-Integer Nonlinear Optimization. Acta Numer. 22, 1–131 (2013)

    Article  MathSciNet  Google Scholar 

  6. Bonami, P., et al.: An algorithmic framework for convex mixed integer nonlinear programs. Discrete Optim. 5(2), 186–204 (2008)

    Article  MathSciNet  Google Scholar 

  7. Bonami, P., Gonçalves, J.: Heuristics for convex mixed integer nonlinear programs. Comput. Optim. Appl. 51(2), 729–747 (2012)

    Article  MathSciNet  Google Scholar 

  8. D’Ambrosio, C., Lodi, A.: Mixed integer nonlinear programming tools: an updated practical overview. Ann. Oper. Res. 24, 301–320 (2013). https://doi.org/10.1007/s10479-012-1272-5

    Article  MathSciNet  MATH  Google Scholar 

  9. Fernandes, F.P.: Programação não linear inteira mista e não convexa sem derivadas. PhD thesis, University of Minho, Braga (2014)

    Google Scholar 

  10. Fernandes, F.P., Costa, M.F.P., Fernandes, E.M.G.P., et al.: Multilocal programming: a derivative-free filter multistart algorithm. In: Murgante, B. (ed.) ICCSA 2013, Part I. LNCS, vol. 7971, pp. 333–346. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39637-3_27

    Chapter  Google Scholar 

  11. Floudas, C., et al.: Handbook of Test Problems in Local and Global Optimization. Kluwer Academic Publishers, Boston (1999)

    Book  Google Scholar 

  12. Hendrix, E.M.T., Tóth, B.G.: Introduction to Nonlinear and Global Optimization. Springer, New York (2010). https://doi.org/10.1007/978-0-387-88670-1

    Book  MATH  Google Scholar 

  13. Romanenko, A., Fernandes, F.P., Fernandes, N.C. P.: PID controllers tuning with MCSFilter. In: AIP Conference Proceedings, vol. 2116, pp. 220003 (2019)

    Google Scholar 

  14. Yang, X.-S.: Optimization Techniques and Applications with Examples. Wiley, Hoboken (2018)

    Book  Google Scholar 

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Acknowledgements

This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UIDB/05757/2020.

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Correspondence to Florbela P. Fernandes .

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Araújo, L., Pacheco, M.F., Rufino, J., Fernandes, F.P. (2021). Towards a High-Performance Implementation of the MCSFilter Optimization Algorithm. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_2

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  • DOI: https://doi.org/10.1007/978-3-030-91885-9_2

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