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Cat-Suite: a collection of optimization problems with categorical and quantitative variables for benchmarking

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This repository contains 60 mixed-variable analytical problems with categorical and quantitative variables for benchmarking. Half of the problems are constrained.

Citation

If you use this benchmark collection in your work, please cite:

Hallé-Hannan, E., Audet, C., Diouane, Y., Le Digabel, S., and Tribes, C. Cat-Suite: A collection of optimization problems with categorical and quantitative variables for benchmarking. Technical Report G-2025-29, Les cahiers du GERAD, 2025. Available at: https://www.gerad.ca/fr/papers/G-2025-39

Summary and problem characteristics

The following two tables present the characteristics of the unconstrained and constrained problems, respectively. The star symbol represents a user-chosen parameter.

Table 1: Unconstrained test problems.

Name $n^{cat}$ $\ell$ $n^{int}$ $n^{cont}$ Smooth Ref. Original problem
Cat-1 2 9 2 $\star$ No [4] Ackley
Cat-2 2 9 2 3 No [4] Beale
Cat-3 2 4 2 2 Yes [7] Augmented-Branin
Cat-4 2 4 2 4 No [4] Bukin-6
Cat-5 1 6 1 3 Yes [5] EVD-52
Cat-6 2 9 0 2 Yes [7] Goldstein
Cat-7 3 16 3 2 No [4] Goldstein-Price
Cat-8 1 4 1 5 Yes [5] HS78
Cat-9 2 9 2 $\star$ No [4] Rastrigin
Cat-10 2 6 2 $\star$ No [4] Rosenbrock
Cat-11 1 4 1 4 Yes [5] Rosen-Suzuki
Cat-12 1 5 $\star$ $\star$ No [4] Styblinski-Tang
Cat-13 1 10 0 4 Yes [6] Toy
Cat-14 1 10 0 8 No [6] Toy
Cat-15 1 5 3 4 Yes [5] Wong-1
Cat-16 2 9 2 $\star$ No [4] Zakharov
Cat-17 2 49 1 7 No [9] Ishigami
Cat-18 2 100 1 7 No [9] Hartmann
Cat-19 2 64 0 $\star$ No [9] Levy
Cat-20 3 80 2 4 No [9] Camel
Cat-21 1 61 0 4 No [5] Gamma
Cat-22 1 51 0 6 Yes [5] EVD-61
Cat-23 3 12 0 5 No [3] Hal-04
Cat-24 1 21 2 3 Yes [5] OET5
Cat-25 1 18 10 10 Yes [5] Wong 3
Cat-26 1 10 3 5 Yes [1] Roustant
Cat-27 2 100 1 4 Yes [1] Kowalik-Osborne
Cat-28 2 36 0 2 Yes [4] Three-Hump
Cat-29 2 64 3 4 Yes [4] McCormick
Cat-30 4 81 4 6 Yes [4] Shekel

Table 2: Constrained test problems.

Name $n^{cat}$ $\ell$ $n^{int}$ $n^{cont}$ $m$ Smooth Ref. Original problem
Cat-cstrs-1 2 9 2 3 3 No [4] Beale
Cat-cstrs-2 2 4 2 2 1 Yes [7] Augmented-Branin
Cat-cstrs-3 2 9 2 4 2 No [4] Bukin-6
Cat-cstrs-4 1 4 4 4 3 Yes [5] Dembo-5
Cat-cstrs-5 1 6 1 3 1 No [5] EVD-52
Cat-cstrs-6 2 9 2 3 4 Yes [1] G-09
Cat-cstrs-7 2 9 0 2 1 Yes [7] Goldstein
Cat-cstrs-8 2 25 2 2 2 Yes [4] Himmelblau
Cat-cstrs-9 2 4 3 5 4 Yes [5] HS-114
Cat-cstrs-10 1 3 2 4 6 Yes [5] Pentagon
Cat-cstrs-11 1 8 2 2 3 Yes [1] Pressure-Vessel
Cat-cstrs-12 2 25 1 2 2 Yes [1] Reinforced-Concrete
Cat-cstrs-13 2 6 2 $\star$ 1 No [4] Rosenbrock
Cat-cstrs-14 1 5 $\star$ $\star$ 2 No [4] Styblinski–Tang
Cat-cstrs-15 1 10 0 4 2 Yes [6] Toy
Cat-cstrs-16 1 6 4 6 3 Yes [5] Wong-2
Cat-cstrs-17 2 64 0 6 11 No [1] Speed-reducer
Cat-cstrs-18 1 50 2 2 5 No [1] Spring
Cat-cstrs-19 6 64 2 2 8 No [1] G07
Cat-cstrs-20 2 100 2 7 10 Yes [1] Car-side-impact
Cat-cstrs-21 1 18 0 16 7 No [5] Dembo-7
Cat-cstrs-22 1 12 0 12 3 No [5] MAD
Cat-cstrs-23 1 13 10 10 4 No [5] Wong 3
Cat-cstrs-24 2 100 2 4 5 No [8] Welded-beam
Cat-cstrs-25 1 10 0 2 3 No [8] Three-bar truss
Cat-cstrs-26 2 36 2 2 6 No [4] Three-hump
Cat-cstrs-27 2 64 3 4 3 No [4] McCormick
Cat-cstrs-28 2 100 2 2 5 No [2] G06
Cat-cstrs-29 4 81 4 6 3 No [4] Shekel
Cat-cstrs-30 2 49 1 7 3 No [9] Ishigami

Best known feasible values: 13 June 2025

Table 3: Best known values for unconstrained problems.

Problem $f(x_{\text{best}})$
Cat-1 21.71
Cat-2 3.12E-12
Cat-3 4.87
Cat-4 1.06E4
Cat-5 -31250.5
Cat-6 38.08
Cat-7 5
Cat-8 -152
Cat-9 -2
Cat-10 1.03
Cat-11 -113.71
Cat-12 -102.51
Cat-13 -0.71
Cat-14 0.14
Cat-15 -1942.82
Cat-16 1

Table 4: Best known feasible values for constrained problems.

Problem $f(x_{\text{best}})$
Cat-cstrs-1 1.27E-03
Cat-cstrs-2 -5.1273
Cat-cstrs-3 4.30E-03
Cat-cstrs-4 -24245741.22
Cat-cstrs-5 -77237.8
Cat-cstrs-6 555.58
Cat-cstrs-7 38.8
Cat-cstrs-8 10
Cat-cstrs-9 -1256527.34
Cat-cstrs-10 1.47E-09
Cat-cstrs-11 6184.75
Cat-cstrs-12 303.4
Cat-cstrs-13 19210.88
Cat-cstrs-14 -66.68
Cat-cstrs-15 3
Cat-cstrs-16 -9721.58

References

[1] A.-S. Crélot, C. Beauthier, D. Orban, C. Sainvitu, and A. Sartenaer. Combining Surrogate Strategies with MADS for Mixed-Variable Derivative-Free Optimization. Technical Report G-2017-70, Les cahiers du GERAD, 2017.

[2] N. I. M. Gould, D. Orban, and Ph. L. Toint. CUTEst: a Constrained and Unconstrained Testing Environment with Safe Threads for Mathematical Optimization. Computational Optimization and Applications, 60(3):545–557, 2015.

[3] M. Halstrup. Black-Box Optimization of Mixed Discrete-Continuous Optimization Problems. PhD thesis, Technical University of Denmark, 2016.

[4] M. Jamil and X.-S. Yang. A literature survey of benchmark functions for global optimisation problems. International Journal of Mathematical Modelling and Numerical Optimisation, 4(2):150–194, 2013.

[5] L. Lukšan and J. Vlček. Test Problems for Nonsmooth Unconstrained and Linearly Constrained Optimization. Technical Report V-798, ICS AS CR, 2000.

[6] M. Munoz Zuniga and D. Sinoquet. Global optimization for mixed categorical-continuous variables based on Gaussian process models with a randomized categorical space exploration step. INFOR: Information Systems and Operational Research, 58(2):310–341, 2020.

[7] J. Pelamatti, L. Brevault, M. Balesdent, E.-G. Talbi, and Y. Guerin. Efficient global optimization of constrained mixed variable problems. Journal of Global Optimization, 73(3):583–613, 2019.

[8] T. Ray and K. M. Liew. A Swarm Metaphor for Multiobjective Design Optimization. Engineering Optimization, 34(2):141–153, 2002.

[9] S. Surjanovic and D. Bingham. Virtual Library of Simulation Experiments: Test Functions and Datasets. Technical Report, Simon Fraser University, 2025.

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