Skip to main content

Advertisement

Log in

Monotonic grey box direct search optimization

  • Original Paper
  • Published:
Optimization Letters Aims and scope Submit manuscript

Abstract

We are interested in blackbox optimization for which the user is aware of monotonic behaviour of some constraints defining the problem. That is, when increasing a variable, the user is able to predict if a function increases or decreases, but is unable to quantify the amount by which it varies. We refer to this type of problems as “monotonic grey box” optimization problems. Our objective is to develop an algorithmic mechanism that exploits this monotonic information to find a feasible solution as quickly as possible. With this goal in mind, we have built a theoretical foundation through a thorough study of monotonicity on cones of multivariate functions. We introduce a trend matrix and a trend direction to guide the Mesh Adaptive Direct Search (Mads) algorithm when optimizing a monotonic grey box optimization problem. Different strategies are tested on a some analytical test problems, and on a real hydroelectric dam optimization problem.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Abramson, M.A., Audet, C., Chrissis, J.W., Walston, J.G.: Mesh adaptive direct search algorithms for mixed variable optimization. Optim. Lett. 3(1), 35–47 (2009)

    Article  MathSciNet  Google Scholar 

  2. Adjengue, L., Audet, C., Ben, Yahia I.: A variance-based method to rank input variables of the mesh adaptive direct search algorithm. Optim. Lett. 8(5), 1599–1610 (2014)

    Article  MathSciNet  Google Scholar 

  3. Amaioua, N., Audet, C., Conn, A.R., Le Digabel, S.: Efficient solution of quadratically constrained quadratic subproblems within a direct-search algorithm. Eur. J. Oper. Res. 268(1), 13–24 (2018)

    Article  MathSciNet  Google Scholar 

  4. Audet, C., Dang, C.-K., Orban, D.: Efficient use of parallelism in algorithmic parameter optimization applications. Optim. Lett. 7(3), 421–433 (2013)

    Article  MathSciNet  Google Scholar 

  5. Audet, C., Dennis Jr., J.E.: Analysis of generalized pattern searches. SIAM J. Optim. 13(3), 889–903 (2003)

    Article  MathSciNet  Google Scholar 

  6. Audet, C., Dennis Jr., J.E.: Mesh adaptive direct search algorithms for constrained optimization. SIAM J. Optim. 17(1), 188–217 (2006)

    Article  MathSciNet  Google Scholar 

  7. Audet, C., Dennis Jr., J.E.: A progressive barrier for derivative-free nonlinear programming. SIAM J. Optim. 20(1), 445–472 (2009)

    Article  MathSciNet  Google Scholar 

  8. Audet, C., Hare, W.: Derivative-Free and Blackbox Optimization. Springer Series in Operations Research and Financial Engineering. Springer, Cham (2017)

    Book  Google Scholar 

  9. Audet, C., Ihaddadene, A., Le Digabel, S., Tribes, C.: Robust optimization of noisy blackbox problems using the mesh adaptive direct search algorithm. Optim. Lett. 12(4), 675–689 (2018)

    Article  MathSciNet  Google Scholar 

  10. Audet, C., Le Digabel, S., Tribes, C.: The mesh adaptive direct search algorithm for granular and discrete variables. SIAM J. Optim. 29(2), 1164–1189 (2019)

    Article  MathSciNet  Google Scholar 

  11. Bigdeli, K., Hare, W., Nutini, J., Tesfamariam, S.: Optimizing damper connectors for adjacent buildings. Optim. Eng. 17(1), 47–75 (2016)

    Article  MathSciNet  Google Scholar 

  12. Borwein, J.M., Burke, J.V., Lewis, A.S.: Differentiability of cone-monotone functions on separable Banach space. Proc. Am. Math. Soc. 132(4), 1067–1076 (2004)

    Article  MathSciNet  Google Scholar 

  13. Boukouvala, F., Floudas, C.A.: ARGONAUT: algoRithms for global optimization of coNstrAined grey-box compUTational problems. Optim. Lett. 11(5), 895–913 (2017)

    Article  MathSciNet  Google Scholar 

  14. Chen, X., Wang, N.: Optimization of short-time gasoline blending scheduling problem with a DNA based hybrid genetic algorithm. Chem. Eng. Process. Process Intensif. 49(10), 1076–1083 (2010)

    Article  Google Scholar 

  15. Diest, K.: Numerical Methods for Metamaterial Design. Topics in Applied Physics, vol. 127. Springer, Berlin (2013)

    Book  Google Scholar 

  16. Hock, W., Schittkowski, K.: Test Examples for Nonlinear Programming Codes. Lecture Notes in Economics and Mathematical Systems, vol. 187. Springer, Berlin (1981)

    Book  Google Scholar 

  17. Larson, J., Menickelly, M., Wild, S.M.: Manifold sampling for \(\ell _1\) nonconvex optimization. SIAM J. Optim. 26(4), 2540–2563 (2016)

    Article  MathSciNet  Google Scholar 

  18. Le Digabel, S.: Algorithm 909: NOMAD: nonlinear optimization with the MADS algorithm. ACM Trans. Math. Softw. 37(4), 44:1–44:15 (2011)

    Article  MathSciNet  Google Scholar 

  19. Lukšan, L., Vlček, J.: Test problems for nonsmooth unconstrained and linearly constrained optimization. Technical report V-798, ICS AS CR (2000)

  20. Moré, J.J., Wild, S.M.: Benchmarking derivative-free optimization algorithms. SIAM J. Optim. 20(1), 172–191 (2009)

    Article  MathSciNet  Google Scholar 

  21. Pigache, F., Messine, F., Nogarede, B.: Optimal design of piezoelectric transformers: a rational approach based on an analytical model and a deterministic global optimization. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 54(7), 1293–1302 (2007)

    Article  Google Scholar 

  22. Poissant, C.: Exploitation d’une structure monotone en recherche directe pour l’optimisation de boîtes grises. Master’s thesis, Polytechnique Montréal (2018). https://publications.polymtl.ca/3006/

  23. Rubinov, A., Tuy, H., Mays, H.: An algorithm for monotonic global optimization problems. Optimization 49(3), 205–221 (2001)

    Article  MathSciNet  Google Scholar 

  24. Sarazin-Mc Cann, L.A.: Opportunisme et ordonnancement en optimisation sans dérivées. Master’s thesis, Polytechnique Montréal (2018). https://publications.polymtl.ca/3099/

  25. Sobieszczanski-Sobieski, J., Agte, J.S., Sandusky, R.R., Jr.: Bi-level integrated system synthesis (BLISS). Technical report NASA/TM-1998-208715, NASA, Langley Research Center (1998)

  26. Tao, J., Wang, N.: DNA double helix based hybrid GA for the gasoline blending recipe optimization problem. Chem. Eng. Technol. 31(3), 440–451 (2008)

    Article  Google Scholar 

  27. Van Dyke, H.A., Vixie, K.R., Asaki, T.J.: Cone monotonicity: structure theorem, properties, and comparisons to other notions of monotonicity. Abstr. Appl. Anal. 1–8, 2013 (2013)

    MathSciNet  MATH  Google Scholar 

  28. Zhao, J., Wang, N.: A bio-inspired algorithm based on membrane computing and its application to gasoline blending scheduling. Comput. Chem. Eng. 35(2), 272–283 (2011)

    Article  Google Scholar 

Download references

Acknowledgements

Thanks to NSERC CRD grant (#RDCPJ 490744-15) with Hydro-Québec and Rio Tinto.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Christophe Tribes.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

The trend matrices for the analytical problems from Sect. 4.1 and the MDO problem from Sect.  4.2 are given as follows:

CHENWANG_F2 [14] (\(n=8,\, m=6\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrrr} 0 &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} \\ 1 &{}\quad -1 &{}\quad 0 &{}\quad 1 &{}\quad {\text{ N }/A} &{}\quad 0 \\ 0 &{}\quad 1 &{}\quad -1 &{}\quad 0 &{}\quad 1 &{}\quad {\text{ N }/A}\\ 1 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad -1 \\ \end{array}\right] \end{aligned}$$

CHENWANG_F3 [14] (\(n=10,\, m=8\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrrrrr} 1 &{}\quad 1 &{}\quad -1 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad -1 \\ 1 &{}\quad -1 &{}\quad 1 &{}\quad {\text{ N }/A} &{}\quad 1 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 1 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 1 &{}\quad {\text{ N }/A} &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad -1 &{}\quad 0 \\ -1 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} \\ 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 \\ \end{array}\right] \end{aligned}$$

HS83 [16] (\(n=5,\, m=6\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrr} -1 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad -1 &{}\quad 1 \\ -1 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 \\ 1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad 1 \\ -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 1 \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad -1 &{}\quad 1 &{}\quad -1 &{}\quad 1 \\ \end{array}\right] \end{aligned}$$

HS114 [19] (\(n=9,\, m=6\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrr} 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 1 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 1 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 1 &{}\quad -1 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad -1 \\ \end{array}\right] \end{aligned}$$

MAD6 [19] (\(n=5, \, m=7\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrrr} -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 1 &{}\quad -1 &{}\quad 1 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 1 &{}\quad 0 \\ \end{array}\right] \end{aligned}$$

PIGACHE [21] (\(n=4, \, m=11\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrrrrrrr} -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad 1 &{}\quad -1 &{}\quad -1 \\ 1 &{}\quad -1 &{}\quad 1 &{}\quad -1 &{}\quad {\text{ N }/A} &{}\quad 1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad 1 \\ -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad 1 &{}\quad -1 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad -1 &{}\quad 0 \\ \end{array}\right] \end{aligned}$$

TAOWANG_F2 [26] (\(n=7,\, m=4\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrr} {\text{ N }/A} &{}\quad 1 &{}\quad 1 &{}\quad {\text{ N }/A} \\ {\text{ N }/A} &{}\quad 1 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad {\text{ N }/A} \\ {\text{ N }/A} &{}\quad 1 &{}\quad 0 &{}\quad 0 \\ {\text{ N }/A} &{}\quad 1 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad 1 \\ 0 &{}\quad 0 &{}\quad -1 &{}\quad -1 \\ \end{array}\right] \end{aligned}$$

ZHAOWANG_F5 [28] (\(n=13,\, m=9\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrrrrr} 1 &{}\quad 1 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 1 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0\\ 0 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 &{}\quad -1\\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0\\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad -1 \\ 1 &{}\quad 1 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 0 \\ 1 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 1 &{}\quad 0 \\ 0 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 1 &{}\quad 0 &{}\quad 0 &{}\quad 1\\ 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 &{}\quad 0 \\ \end{array}\right] \end{aligned}$$

MDO [25] (\(n=10,\, m=10\))

$$\begin{aligned} T= \left[ \begin{array}{rrrrrrrrrr} 1 &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 \\ 1 &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 \\ -1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad 0 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad 1 \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 1 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 \\ -1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad -1 &{}\quad 0 &{}\quad 1 &{}\quad -1 &{}\quad 1 &{}\quad -1 \\ 1 &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad -1 &{}\quad 1 &{}\quad -1 &{}\quad 1 \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 0 &{}\quad 0 \\ {\text{ N }/A} &{}\quad {\text{ N }/A} &{}\quad 1 &{}\quad 1 &{}\quad 1 &{}\quad 0 &{}\quad -1 &{}\quad 1 &{}\quad 0 &{}\quad 0 \\ \end{array}\right] . \end{aligned}$$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Audet, C., Côté, P., Poissant, C. et al. Monotonic grey box direct search optimization. Optim Lett 14, 3–18 (2020). https://doi.org/10.1007/s11590-019-01497-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11590-019-01497-8

Keywords

Navigation