Test functions for optimization

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In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as:

  • Convergence rate.
  • Precision.
  • Robustness.
  • General performance.

Here some test functions are presented with the aim of giving an idea about the different situations that optimization algorithms have to face when coping with these kinds of problems. In the first part, some objective functions for single-objective optimization cases are presented. In the second part, test functions with their respective Pareto fronts for multi-objective optimization problems (MOP) are given.

The artificial landscapes presented herein for single-objective optimization problems are taken from Bäck,[1] Haupt et al.[2] and from Rody Oldenhuis software.[3] Given the number of problems (55 in total), just a few are presented here. The complete list of test functions is found on the Mathworks website.[4]

The test functions used to evaluate the algorithms for MOP were taken from Deb,[5] Binh et al.[6] and Binh.[7] You can download the software developed by Deb,[8] which implements the NSGA-II procedure with GAs, or the program posted on Internet,[9] which implements the NSGA-II procedure with ES.

Just a general form of the equation, a plot of the objective function, boundaries of the object variables and the coordinates of global minima are given herein.

Test functions for single-objective optimization[edit]

Name Plot Formula Global minimum Search domain
Rastrigin function Rastrigin function for n=2

Ackley's function Ackley's function for n=2

Sphere function Sphere function for n=2 ,
Rosenbrock function Rosenbrock's function for n=2 ,
Beale's function Beale's function

Goldstein–Price function Goldstein–Price function

Booth's function Booth's function
Bukin function N.6 Bukin function N.6 ,
Matyas function Matyas function
Lévi function N.13 Lévi function N.13

Three-hump camel function Three Hump Camel function
Easom function Easom function
Cross-in-tray function Cross-in-tray function
Eggholder function Eggholder function
Hölder table function Holder table function
McCormick function McCormick function ,
Schaffer function N. 2 Schaffer function N.2
Schaffer function N. 4 Schaffer function N.4
Styblinski–Tang function Styblinski-Tang function , ..

Test functions for constrained optimization[edit]

Name Plot Formula Global minimum Search domain
Rosenbrock function constrained with a cubic and a line[10] Rosenbrock function constrained with a cubic and a line ,

subjected to:

Rosenbrock function constrained to a disk[11] Rosenbrock function constrained to a disk ,

subjected to:

Mishra's Bird function - constrained[12] Bird function (constrained) ,

subjected to:

Townsend function[13] Heart constrained multimodal function ,

subjected to: where: t = Atan2(y/x)

Simionescu function[14] Simionescu function ,

subjected to:

Test functions for multi-objective optimization[edit]

[further explanation needed]

Name Plot Functions Constraints Search domain
Binh and Korn function: Binh and Korn function ,
Chakong and Haimes function: Chakong and Haimes function
Fonseca and Fleming function:[15] Fonseca and Fleming function ,
Test function 4:[7] Test function 4.[7]
Kursawe function:[16] Kursawe function , .
Schaffer function N. 1:[17] Schaffer function N.1 . Values of from to have been used successfully. Higher values of increase the difficulty of the problem.
Schaffer function N. 2: Schaffer function N.2 .
Poloni's two objective function: Poloni's two objective function

Zitzler–Deb–Thiele's function N. 1: Zitzler-Deb-Thiele's function N.1 , .
Zitzler–Deb–Thiele's function N. 2: Zitzler-Deb-Thiele's function N.2 , .
Zitzler–Deb–Thiele's function N. 3: Zitzler-Deb-Thiele's function N.3 , .
Zitzler–Deb–Thiele's function N. 4: caption2  = Zitzler-Deb-Thiele's function N.4 , ,
Zitzler–Deb–Thiele's function N. 6: Zitzler-Deb-Thiele's function N.6 , .
Viennet function: Viennet function .
Osyczka and Kundu function: Osyczka and Kundu function , , .
CTP1 function (2 variables):[5] CTP1 function (2 variables).[5] .
Constr-Ex problem:[5] Constr-Ex problem.[5] ,

See also[edit]


  1. ^ Bäck, Thomas (1995). Evolutionary algorithms in theory and practice : evolution strategies, evolutionary programming, genetic algorithms. Oxford: Oxford University Press. p. 328. ISBN 0-19-509971-0. 
  2. ^ Haupt, Randy L. Haupt, Sue Ellen (2004). Practical genetic algorithms with CD-Rom (2nd ed.). New York: J. Wiley. ISBN 0-471-45565-2. 
  3. ^ Oldenhuis, Rody. "Many test functions for global optimizers". Mathworks. Retrieved 1 November 2012. 
  4. ^ Ortiz, Gilberto A. "Evolution Strategies (ES)". Mathworks. Retrieved 1 November 2012. 
  5. ^ a b c d e Deb, Kalyanmoy (2002) Multiobjective optimization using evolutionary algorithms (Repr. ed.). Chichester [u.a.]: Wiley. ISBN 0-471-87339-X.
  6. ^ Binh T. and Korn U. (1997) MOBES: A Multiobjective Evolution Strategy for Constrained Optimization Problems. In: Proceedings of the Third International Conference on Genetic Algorithms. Czech Republic. pp. 176–182
  7. ^ a b c Binh T. (1999) A multiobjective evolutionary algorithm. The study cases. Technical report. Institute for Automation and Communication. Barleben, Germany
  8. ^ Deb K. (2011) Software for multi-objective NSGA-II code in C. Available at URL:http://www.iitk.ac.in/kangal/codes.shtml. Revision 1.1.6
  9. ^ Ortiz, Gilberto A. "Multi-objective optimization using ES as Evolutionary Algorithm.". Mathworks. Retrieved 1 November 2012. 
  10. ^ Simionescu, P.A.; Beale, D. (September 29 – October 2, 2002). New Concepts in Graphic Visualization of Objective Functions (PDF). ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Montreal, Canada. pp. 891–897. Retrieved 7 January 2017. 
  11. ^ https://www.mathworks.com/help/optim/ug/example-nonlinear-constrained-minimization.html?requestedDomain=www.mathworks.com
  12. ^ http://www.phoenix-int.com/software/benchmark_report/bird_constrained.php
  13. ^ http://www.chebfun.org/examples/opt/ConstrainedOptimization.html
  14. ^ Simionescu, P.A. (2014). Computer Aided Graphing and Simulation Tools for AutoCAD Users (1st ed.). Boca Raton, FL: CRC Press. ISBN 978-1-4822-5290-3. 
  15. ^ C. M. Fonzeca and P. J. Fleming, “An overview of evolutionary algorithms in multiobjective optimization,” Evol Comput, vol. 3, no. 1, pp. 1–16, 1995.
  16. ^ F. Kursawe, “A variant of evolution strategies for vector optimization,” in PPSN I, Vol 496 Lect Notes in Comput Sc. Springer-Verlag, 1991, pp. 193–197.
  17. ^ J. D. Schaffer, “Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition),” Ph.D. dissertation, Vanderbilt University, 1984.

External links[edit]