Test functions for optimization

Functions used to evaluate optimization algorithms From Wikipedia, the free encyclopedia

In applied mathematics, test functions, known as artificial landscapes, are useful to evaluate characteristics of optimization algorithms, such as convergence rate, precision, robustness and 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 test functions used to evaluate the algorithms for MOP were taken from Deb,[4] Binh et al.[5] and Binh.[6] The software developed by Deb can be downloaded,[7] which implements the NSGA-II procedure with GAs, or the program posted on Internet,[8] 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

More information , ...
Name Plot Formula Global minimum Search domain
Rastrigin function Rastrigin function for n=2

Ackley function Ackley's function for n=2

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

Goldstein–Price function Goldstein–Price function

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

Griewank function Griewank's function , where ,
Himmelblau's function Himmelblau's function
Three-hump camel function Three Hump Camel function
Easom function Easom function
Cross-in-tray function Cross-in-tray function
Eggholder function[9][10] 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 , ..
Shekel function A Shekel function in 2 dimensions and with 10 maxima

or, similarly,

,
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Test functions for constrained optimization

More information , subjected to: ...
NamePlotFormulaGlobal minimumSearch domain
Rosenbrock function constrained to a disk[11] Thumb ,

subjected to:

,
Mishra's Bird function - constrained[12][13] Thumb ,

subjected to:

,
Townsend function (modified)[14] Thumb ,

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

,
Keane's bump function[15] Thumb ,

subjected to: , and

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Test functions for multi-objective optimization

[further explanation needed]

More information , ...
NamePlotFunctionsConstraintsSearch domain
Binh and Korn function:[5] Thumb ,
Chankong and Haimes function:[16] Thumb
Fonseca–Fleming function:[17] Thumb ,
Test function 4:[6] Thumb
Kursawe function:[18] Thumb , .
Schaffer function N. 1:[19] Thumb . Values of from to have been used successfully. Higher values of increase the difficulty of the problem.
Schaffer function N. 2: Thumb .
Poloni's two objective function: Thumb

Zitzler–Deb–Thiele's function N. 1:[20] Thumb , .
Zitzler–Deb–Thiele's function N. 2:[20] Thumb , .
Zitzler–Deb–Thiele's function N. 3:[20] Thumb , .
Zitzler–Deb–Thiele's function N. 4:[20] Thumb , ,
Zitzler–Deb–Thiele's function N. 6:[20] Thumb , .
Osyczka and Kundu function:[21] Thumb , , .
CTP1 function (2 variables):[4][22] Thumb .
Constr-Ex problem:[4] Thumb ,
Viennet function: Thumb .
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References

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