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

Under construction!

This repository contains 32 mixed-variable analytical problems with categorical and quantitative variables for benchmarking. Half of the problems are constrained.

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 $n^{\text{cat}}$ $\ell$ $n^{\text{int}}$ $n^{\text{cont}}$ Smooth Ref. Original problem
Cat-1 2 9 2 $\star$ No [2] Ackley
Cat-2 2 9 2 3 No [2] Beale
Cat-3 2 4 2 2 Yes [5] Augmented-Branin
Cat-4 2 4 2 4 No [2] Bukin-6
Cat-5 1 6 1 3 Yes [3] EVD-52
Cat-6 2 9 0 2 Yes [5] Goldstein
Cat-7 3 16 3 2 No [2] Goldstein-Price
Cat-8 1 4 1 5 Yes [3] HS78
Cat-9 2 9 2 $\star$ No [2] Rastrigin
Cat-10 2 6 2 $\star$ No [2] Rosenbrock
Cat-11 1 4 1 4 Yes [3] Rosen-Suzuki
Cat-12 1 5 $\star$ $\star$ No [2] Styblinski-Tang
Cat-13 1 10 0 4 Yes [4] Toy
Cat-14 1 10 0 8 No [4] Toy
Cat-15 1 5 3 4 Yes [3] Wong-1
Cat-16 2 9 2 $e$ No [2] Zakharov

Table 2: Constrained test problems.

Name $n^{\text{cat}}$ $\ell$ $n^{\text{int}}$ $n^{\text{cont}}$ $m$ Smooth Ref. Original problem
Cat-cstrs-1 2 9 2 3 3 No [2] Beale
Cat-cstrs-2 2 4 2 2 1 Yes [5] Augmented-Branin
Cat-cstrs-3 2 9 2 4 2 No [2] Bukin-6
Cat-cstrs-4 1 4 4 4 3 Yes [3] Dembo-5
Cat-cstrs-5 1 6 1 3 1 No [3] EVD-52
Cat-cstrs-6 2 9 2 3 4 Yes [1] G-09
Cat-cstrs-7 2 9 0 2 1 Yes [5] Goldstein
Cat-cstrs-8 2 25 2 2 2 Yes [2] Himmelblau
Cat-cstrs-9 2 4 3 5 4 Yes [3] HS-114
Cat-cstrs-10 1 3 2 4 6 Yes [3] 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 [2] Rosenbrock
Cat-cstrs-14 1 5 $\star$ $\star$ 2 No [2] Styblinski–Tang
Cat-cstrs-15 1 10 0 4 2 Yes [4] Toy
Cat-cstrs-16 1 6 4 6 3 Yes [3] Wong-2

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] 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.

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

[4] 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.

[5] 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.

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