Test functions for optimization

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

Velocity of convergence.
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 amount 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 problems

Name	Formula	Minimum	Search domain
Rastrigin function	$f(\mathbf {x} )=An+\sum _{i=1}^{n}\left[x_{i}^{2}-A\cos(2\pi x_{i})\right]$ ${\text{where: }}A=10$	$f(0,0)=0$	$-5.12\leq x,y\leq 5.12$
Ackley's function	$f(x,y)=-20\exp \left(-0.2{\sqrt {0.5\left(x^{2}+y^{2}\right)}}\right)$ $-\exp \left(0.5\left(\cos \left(2\pi x\right)+\cos \left(2\pi y\right)\right)\right)+e+20$	$f(0,0)=0$	$-5\leq x,y\leq 5$
Sphere function	$f({\boldsymbol {x}})=\sum _{i=1}^{n}x_{i}^{2}$	$f(x_{1},\dots ,x_{n})=f(0,\dots ,0)=0$	$-\infty \leq x_{i}\leq \infty$ , $1\leq i\leq n$
Rosenbrock function	$f({\boldsymbol {x}})=\sum _{i=1}^{n-1}\left[100\left(x_{i+1}-x_{i}^{2}\right)^{2}+\left(x_{i}-1\right)^{2}\right]$	${\text{Min}}={\begin{cases}n=2&\rightarrow \quad f(1,1)=0,\\n=3&\rightarrow \quad f(1,1,1)=0,\\n>3&\rightarrow \quad f\left(\underbrace {1,\dots ,1} _{(n){\text{ times}}}\right)=0\\\end{cases}}$	$-\infty \leq x_{i}\leq \infty$ , $1\leq i\leq n$
Beale's function	$f(x,y)=\left(1.5-x+xy\right)^{2}+\left(2.25-x+xy^{2}\right)^{2}$ $+\left(2.625-x+xy^{3}\right)^{2}$	$f(3,0.5)=0$	$-4.5\leq x,y\leq 4.5$
Goldstein–Price function	$f(x,y)=\left(1+\left(x+y+1\right)^{2}\left(19-14x+3x^{2}-14y+6xy+3y^{2}\right)\right)$ $\left(30+\left(2x-3y\right)^{{2}}\left(18-32x+12x^{{2}}+48y-36xy+27y^{{2}}\right)\right)$	$f(0,-1)=3$	$-2\leq x,y\leq 2$
Booth's function	$f(x,y)=\left(x+2y-7\right)^{2}+\left(2x+y-5\right)^{2}$	$f(1,3)=0$	$-10\leq x,y\leq 10$
Bukin function N.6	$f(x,y)=100{\sqrt {\left\|y-0.01x^{2}\right\|}}+0.01\left\|x+10\right\|.\quad$	$f(-10,1)=0$	$-15\leq x\leq -5$ , $-3\leq y\leq 3$
Matyas function	$f(x,y)=0.26\left(x^{2}+y^{2}\right)-0.48xy$	$f(0,0)=0$	$-10\leq x,y\leq 10$
Lévi function N.13	$f(x,y)=\sin ^{2}\left(3\pi x\right)+\left(x-1\right)^{2}\left(1+\sin ^{2}\left(3\pi y\right)\right)$ $+\left(y-1\right)^{2}\left(1+\sin ^{2}\left(2\pi y\right)\right)$	$f(1,1)=0$	$-10\leq x,y\leq 10$
Three-hump camel function	$f(x,y)=2x^{2}-1.05x^{4}+{\frac {x^{6}}{6}}+xy+y^{2}$	$f(0,0)=0$	$-5\leq x,y\leq 5$
Easom function	$f(x,y)=-\cos \left(x\right)\cos \left(y\right)\exp \left(-\left(\left(x-\pi \right)^{2}+\left(y-\pi \right)^{2}\right)\right)$	$f(\pi ,\pi )=-1$	$-100\leq x,y\leq 100$
Cross-in-tray function	$f(x,y)=-0.0001\left(\left\|\sin \left(x\right)\sin \left(y\right)\exp \left(\left\|100-{\frac {\sqrt {x^{2}+y^{2}}}{\pi }}\right\|\right)\right\|+1\right)^{0.1}$	${\text{Min}}={\begin{cases}f\left(1.34941,-1.34941\right)&=-2.06261\\f\left(1.34941,1.34941\right)&=-2.06261\\f\left(-1.34941,1.34941\right)&=-2.06261\\f\left(-1.34941,-1.34941\right)&=-2.06261\\\end{cases}}$	$-10\leq x,y\leq 10$
Eggholder function	$f(x,y)=-\left(y+47\right)\sin \left({\sqrt {\left\|{\frac {x}{2}}+\left(y+47\right)\right\|}}\right)-x\sin \left({\sqrt {\left\|x-\left(y+47\right)\right\|}}\right)$	$f(512,404.2319)=-959.6407$	$-512\leq x,y\leq 512$
Hölder table function	$f(x,y)=-\left\|\sin \left(x\right)\cos \left(y\right)\exp \left(\left\|1-{\frac {\sqrt {x^{2}+y^{2}}}{\pi }}\right\|\right)\right\|$	${\text{Min}}={\begin{cases}f\left(8.05502,9.66459\right)&=-19.2085\\f\left(-8.05502,9.66459\right)&=-19.2085\\f\left(8.05502,-9.66459\right)&=-19.2085\\f\left(-8.05502,-9.66459\right)&=-19.2085\end{cases}}$	$-10\leq x,y\leq 10$
McCormick function	$f(x,y)=\sin \left(x+y\right)+\left(x-y\right)^{2}-1.5x+2.5y+1$	$f(-0.54719,-1.54719)=-1.9133$	$-1.5\leq x\leq 4$ , $-3\leq y\leq 4$
Schaffer function N. 2	$f(x,y)=0.5+{\frac {\sin ^{2}\left(x^{2}-y^{2}\right)-0.5}{\left(1+0.001\left(x^{2}+y^{2}\right)\right)^{2}}}$	$f(0,0)=0$	$-100\leq x,y\leq 100$
Schaffer function N. 4	$f(x,y)=0.5+{\frac {\cos ^{2}\left(\sin \left(\left\|x^{2}-y^{2}\right\|\right)\right)-0.5}{\left(1+0.001\left(x^{2}+y^{2}\right)\right)^{2}}}$	$f(0,1.25313)=0.292579$	$-100\leq x,y\leq 100$
Styblinski–Tang function	$f({\boldsymbol {x}})={\frac {\sum _{i=1}^{n}x_{i}^{4}-16x_{i}^{2}+5x_{i}}{2}}$	$-39.16617n<f\left(\underbrace {-2.903534,\ldots ,-2.903534} _{(n){\text{ times}}}\right)<-39.16616n$	$-5\leq x_{i}\leq 5$ , $1\leq i\leq n$ .
Simionescu function^[10]	$f(x,y)=0.1xy$ , ${\text{subjected to: }}x^{2}+y^{2}\leq \left(r_{T}+r_{S}\cos \left(n\arctan {\frac {x}{y}}\right)\right)^{2}$ ${\text{where: }}r_{T}=1,r_{S}=0.2{\text{ and }}n=8$	$f(\pm 0.85586214,\mp 0.85586214)=-0.072625$	$-1.25\leq x,y\leq 1.25$

Test functions for multi-objective optimization problems

Name	Functions	Constraints	Search domain
Binh and Korn function:	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=4x^{2}+4y^{2}\\f_{2}\left(x,y\right)&=\left(x-5\right)^{2}+\left(y-5\right)^{2}\\\end{cases}}$	${\text{s.t.}}={\begin{cases}g_{1}\left(x,y\right)&=\left(x-5\right)^{2}+y^{2}\leq 25\\g_{2}\left(x,y\right)&=\left(x-8\right)^{2}+\left(y+3\right)^{2}\geq 7.7\\\end{cases}}$	$0\leq x\leq 5$ , $0\leq y\leq 3$
Chakong and Haimes function:	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=2+\left(x-2\right)^{2}+\left(y-1\right)^{2}\\f_{2}\left(x,y\right)&=9x-\left(y-1\right)^{2}\\\end{cases}}$	${\text{s.t.}}={\begin{cases}g_{1}\left(x,y\right)&=x^{2}+y^{2}\leq 225\\g_{2}\left(x,y\right)&=x-3y+10\leq 0\\\end{cases}}$	$-20\leq x,y\leq 20$
Fonseca and Fleming function:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=1-\exp \left(-\sum _{i=1}^{n}\left(x_{i}-{\frac {1}{\sqrt {n}}}\right)^{2}\right)\\f_{2}\left({\boldsymbol {x}}\right)&=1-\exp \left(-\sum _{i=1}^{n}\left(x_{i}+{\frac {1}{\sqrt {n}}}\right)^{2}\right)\\\end{cases}}$		$-4\leq x_{i}\leq 4$ , $1\leq i\leq n$
Test function 4:^[7]	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=x^{2}-y\\f_{2}\left(x,y\right)&=-0.5x-y-1\\\end{cases}}$	${\text{s.t.}}={\begin{cases}g_{1}\left(x,y\right)&=6.5-{\frac {x}{6}}-y\geq 0\\g_{2}\left(x,y\right)&=7.5-0.5x-y\geq 0\\g_{3}\left(x,y\right)&=30-5x-y\geq 0\\\end{cases}}$	$-7\leq x,y\leq 4$
Kursawe function:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=\sum _{i=1}^{2}\left[-10\exp \left(-0.2{\sqrt {x_{i}^{2}+x_{i+1}^{2}}}\right)\right]\\&\\f_{2}\left({\boldsymbol {x}}\right)&=\sum _{i=1}^{3}\left[\left\|x_{i}\right\|^{0.8}+5\sin \left(x_{i}^{3}\right)\right]\\\end{cases}}$		$-5\leq x_{i}\leq 5$ , $1\leq i\leq 3$ .
Schaffer function N. 1:	${\text{Minimize}}={\begin{cases}f_{1}\left(x\right)&=x^{2}\\f_{2}\left(x\right)&=\left(x-2\right)^{2}\\\end{cases}}$		$-A\leq x\leq A$ . Values of $A$ from $10$ to $10^{{5}}$ have been used successfully. Higher values of $A$ increase the difficulty of the problem.
Schaffer function N. 2:	${\text{Minimize}}={\begin{cases}f_{1}\left(x\right)&={\begin{cases}-x,&{\text{if }}x\leq 1\\x-2,&{\text{if }}1<x\leq 3\\4-x,&{\text{if }}3<x\leq 4\\x-4,&{\text{if }}x>4\\\end{cases}}\\f_{2}\left(x\right)&=\left(x-5\right)^{2}\\\end{cases}}$		$-5\leq x\leq 10$ .
Poloni's two objective function:	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=\left[1+\left(A_{1}-B_{1}\left(x,y\right)\right)^{2}+\left(A_{2}-B_{2}\left(x,y\right)\right)^{2}\right]\\f_{2}\left(x,y\right)&=\left(x+3\right)^{2}+\left(y+1\right)^{2}\\\end{cases}}$ ${\text{where}}={\begin{cases}A_{1}&=0.5\sin \left(1\right)-2\cos \left(1\right)+\sin \left(2\right)-1.5\cos \left(2\right)\\A_{2}&=1.5\sin \left(1\right)-\cos \left(1\right)+2\sin \left(2\right)-0.5\cos \left(2\right)\\B_{1}\left(x,y\right)&=0.5\sin \left(x\right)-2\cos \left(x\right)+\sin \left(y\right)-1.5\cos \left(y\right)\\B_{2}\left(x,y\right)&=1.5\sin \left(x\right)-\cos \left(x\right)+2\sin \left(y\right)-0.5\cos \left(y\right)\end{cases}}$		$-\pi \leq x,y\leq \pi$
Zitzler–Deb–Thiele's function N. 1:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=x_{1}\\f_{2}\left({\boldsymbol {x}}\right)&=g\left({\boldsymbol {x}}\right)h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)\\g\left({\boldsymbol {x}}\right)&=1+{\frac {9}{29}}\sum _{i=2}^{30}x_{i}\\h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)&=1-{\sqrt {\frac {f_{1}\left({\boldsymbol {x}}\right)}{g\left({\boldsymbol {x}}\right)}}}\\\end{cases}}$		$0\leq x_{i}\leq 1$ , $1\leq i\leq 30$ .
Zitzler–Deb–Thiele's function N. 2:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=x_{1}\\f_{2}\left({\boldsymbol {x}}\right)&=g\left({\boldsymbol {x}}\right)h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)\\g\left({\boldsymbol {x}}\right)&=1+{\frac {9}{29}}\sum _{i=2}^{30}x_{i}\\h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)&=1-\left({\frac {f_{1}\left({\boldsymbol {x}}\right)}{g\left({\boldsymbol {x}}\right)}}\right)^{2}\\\end{cases}}$		$0\leq x_{i}\leq 1$ , $1\leq i\leq 30$ .
Zitzler–Deb–Thiele's function N. 3:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=x_{1}\\f_{2}\left({\boldsymbol {x}}\right)&=g\left({\boldsymbol {x}}\right)h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)\\g\left({\boldsymbol {x}}\right)&=1+{\frac {9}{29}}\sum _{i=2}^{30}x_{i}\\h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)&=1-{\sqrt {\frac {f_{1}\left({\boldsymbol {x}}\right)}{g\left({\boldsymbol {x}}\right)}}}-\left({\frac {f_{1}\left({\boldsymbol {x}}\right)}{g\left({\boldsymbol {x}}\right)}}\right)\sin \left(10\pi f_{1}\left({\boldsymbol {x}}\right)\right)\end{cases}}$		$0\leq x_{i}\leq 1$ , $1\leq i\leq 30$ .
Zitzler–Deb–Thiele's function N. 4:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=x_{1}\\f_{2}\left({\boldsymbol {x}}\right)&=g\left({\boldsymbol {x}}\right)h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)\\g\left({\boldsymbol {x}}\right)&=91+\sum _{i=2}^{10}\left(x_{i}^{2}-10\cos \left(4\pi x_{i}\right)\right)\\h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)&=1-{\sqrt {\frac {f_{1}\left({\boldsymbol {x}}\right)}{g\left({\boldsymbol {x}}\right)}}}\end{cases}}$		$0\leq x_{1}\leq 1$ , $-5\leq x_{i}\leq 5$ , $2\leq i\leq 10$
Zitzler–Deb–Thiele's function N. 6:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=1-\exp \left(-4x_{1}\right)\sin ^{6}\left(6\pi x_{1}\right)\\f_{2}\left({\boldsymbol {x}}\right)&=g\left({\boldsymbol {x}}\right)h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)\\g\left({\boldsymbol {x}}\right)&=1+9\left[{\frac {\sum _{i=2}^{10}x_{i}}{9}}\right]^{0.25}\\h\left(f_{1}\left({\boldsymbol {x}}\right),g\left({\boldsymbol {x}}\right)\right)&=1-\left({\frac {f_{1}\left({\boldsymbol {x}}\right)}{g\left({\boldsymbol {x}}\right)}}\right)^{2}\\\end{cases}}$		$0\leq x_{i}\leq 1$ , $1\leq i\leq 10$ .
Viennet function:	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=0.5\left(x^{2}+y^{2}\right)+\sin \left(x^{2}+y^{2}\right)\\f_{2}\left(x,y\right)&={\frac {\left(3x-2y+4\right)^{2}}{8}}+{\frac {\left(x-y+1\right)^{2}}{27}}+15\\f_{3}\left(x,y\right)&={\frac {1}{x^{2}+y^{2}+1}}-1.1\exp \left(-\left(x^{2}+y^{2}\right)\right)\\\end{cases}}$		$-3\leq x,y\leq 3$ .
Osyczka and Kundu function:	${\text{Minimize}}={\begin{cases}f_{1}\left({\boldsymbol {x}}\right)&=-25\left(x_{1}-2\right)^{2}-\left(x_{2}-2\right)^{2}-\left(x_{3}-1\right)^{2}-\left(x_{4}-4\right)^{2}-\left(x_{5}-1\right)^{2}\\f_{2}\left({\boldsymbol {x}}\right)&=\sum _{i=1}^{6}x_{i}^{2}\\\end{cases}}$	${\text{s.t.}}={\begin{cases}g_{1}\left({\boldsymbol {x}}\right)&=x_{1}+x_{2}-2\geq 0\\g_{2}\left({\boldsymbol {x}}\right)&=6-x_{1}-x_{2}\geq 0\\g_{3}\left({\boldsymbol {x}}\right)&=2-x_{2}+x_{1}\geq 0\\g_{4}\left({\boldsymbol {x}}\right)&=2-x_{1}+3x_{2}\geq 0\\g_{5}\left({\boldsymbol {x}}\right)&=4-\left(x_{3}-3\right)^{2}-x_{4}\geq 0\\g_{6}\left({\boldsymbol {x}}\right)&=\left(x_{5}-3\right)^{2}+x_{6}-4\geq 0\end{cases}}$	$0\leq x_{1},x_{2},x_{6}\leq 10$ , $1\leq x_{3},x_{5}\leq 5$ , $0\leq x_{4}\leq 6$ .
CTP1 function (2 variables):^[5]	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=x\\f_{2}\left(x,y\right)&=\left(1+y\right)\exp \left(-{\frac {x}{1+y}}\right)\end{cases}}$	${\text{s.t.}}={\begin{cases}g_{1}\left(x,y\right)&={\frac {f_{2}\left(x,y\right)}{0.858\exp \left(-0.541f_{1}\left(x,y\right)\right)}}\geq 1\\g_{1}\left(x,y\right)&={\frac {f_{2}\left(x,y\right)}{0.728\exp \left(-0.295f_{1}\left(x,y\right)\right)}}\geq 1\end{cases}}$	$0\leq x,y\leq 1$ .
Constr-Ex problem:^[5]	${\text{Minimize}}={\begin{cases}f_{1}\left(x,y\right)&=x\\f_{2}\left(x,y\right)&={\frac {1+y}{x}}\\\end{cases}}$	${\text{s.t.}}={\begin{cases}g_{1}\left(x,y\right)&=y+9x\geq 6\\g_{1}\left(x,y\right)&=-y+9x\geq 1\\\end{cases}}$	$0.1\leq x\leq 1$ , $0\leq y\leq 5$

External links

Virtual Library of Simulation Experiments: Test Functions and Datasets

References

↑ 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.
↑ Haupt, Randy L. Haupt, Sue Ellen (2004). Practical genetic algorithms with CD-Rom (2nd ed.). New York: J. Wiley. ISBN 0-471-45565-2.
↑ Oldenhuis, Rody. "Many test functions for global optimizers". Mathworks. Retrieved 1 November 2012.
↑ Ortiz, Gilberto A. "Evolution Strategies (ES)". Mathworks. Retrieved 1 November 2012.
1 2 3 4 5 Deb, Kalyanmoy (2002) Multiobjective optimization using evolutionary algorithms (Repr. ed.). Chichester [u.a.]: Wiley. ISBN 0-471-87339-X.
↑ 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
1 2 3 Binh T. (1999) A multiobjective evolutionary algorithm. The study cases. Technical report. Institute for Automation and Communication. Barleben, Germany
↑ 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
↑ Ortiz, Gilberto A. "Multi-objective optimization using ES as Evolutionary Algorithm.". Mathworks. Retrieved 1 November 2012.
↑ 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.

This article is issued from Wikipedia - version of the 10/7/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.

Test functions for optimization

Test functions for single-objective optimization problems

Test functions for multi-objective optimization problems

See also

External links

References