Photon Structure Function

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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 kind 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 Plot Formula Minimum Search domain
Ackley's function: Ackley's function for n=2 f(x,y)=−20exp⁡(−0.20.5(x2+y2))

−exp⁡(0.5(cos⁡(2πx)+cos⁡(2πy)))+20+e.

f(0,0)=0 −5≤x,y≤5
Sphere function Sphere function for n=2 f(𝒙)=∑i=1nxi2. f(x1,…,xn)=f(0,…,0)=0 −∞≤xi≤∞, 1≤i≤n
Rosenbrock function Rosenbrock's function for n=2 f(𝒙)=∑i=1n−1[100(xi+1−xi2)2+(xi−1)2]. Min={n=2→f(1,1)=0,n=3→f(1,1,1)=0,n>3→f(−1,1,…,1⏟(n−1) times)=0. −∞≤xi≤∞, 1≤i≤n
Beale's function Beale's function f(x,y)=(1.5−x+xy)2+(2.25−x+xy2)2

+(2.625−x+xy3)2.

f(3,0.5)=0 −4.5≤x,y≤4.5
Goldstein–Price function: Goldstein–Price function f(x,y)=(1+(x+y+1)2(19−14x+3x2−14y+6xy+3y2))

(30+(2x−3y)2(18−32x+12x2+48y−36xy+27y2)).

f(0,−1)=3 −2≤x,y≤2
Booth's function: Booth's function f(x,y)=(x+2y−7)2+(2x+y−5)2. f(1,3)=0 −10≤x,y≤10.
Bukin function N.6: Bukin function N.6 f(x,y)=100|y−0.01x2|+0.01|x+10|. f(−10,1)=0 −15≤x≤−5, −3≤y≤3
Matyas function: Matyas function f(x,y)=0.26(x2+y2)−0.48xy. f(0,0)=0 −10≤x,y≤10
Lévi function N.13: Lévi function N.13 f(x,y)=sin2(3πx)+(x−1)2(1+sin2(3πy))

+(y−1)2(1+sin2(2πy)).

f(1,1)=0 −10≤x,y≤10
Three-hump camel function: Three Hump Camel function f(x,y)=2x2−1.05x4+x66+xy+y2. f(0,0)=0 −5≤x,y≤5
Easom function: Easom function f(x,y)=−cos⁡(x)cos⁡(y)exp⁡(−((x−π)2+(y−π)2)). f(π,π)=−1 −100≤x,y≤100
Cross-in-tray function: Cross-in-tray function f(x,y)=−0.0001(|sin⁡(x)sin⁡(y)exp⁡(|100−x2+y2π|)|+1)0.1. Min={f(1.34941,−1.34941)=−2.06261f(1.34941,1.34941)=−2.06261f(−1.34941,1.34941)=−2.06261f(−1.34941,−1.34941)=−2.06261 −10≤x,y≤10
Eggholder function: Eggholder function f(x,y)=−(y+47)sin⁡(|y+x2+47|)−xsin⁡(|x−(y+47)|). f(512,404.2319)=−959.6407 −512≤x,y≤512
Hölder table function: Holder table function f(x,y)=−|sin⁡(x)cos⁡(y)exp⁡(|1−x2+y2π|)|. Min={f(8.05502,9.66459)=−19.2085f(−8.05502,9.66459)=−19.2085f(8.05502,−9.66459)=−19.2085f(−8.05502,−9.66459)=−19.2085 −10≤x,y≤10
McCormick function: McCormick function f(x,y)=sin⁡(x+y)+(x−y)2−1.5x+2.5y+1. f(−0.54719,−1.54719)=−1.9133 −1.5≤x≤4, −3≤y≤4
Schaffer function N. 2: Schaffer function N.2 f(x,y)=0.5+sin2(x2−y2)−0.5(1+0.001(x2+y2))2. f(0,0)=0 −100≤x,y≤100
Schaffer function N. 4: Schaffer function N.4 f(x,y)=0.5+cos⁡(sin⁡(|x2−y2|))−0.5(1+0.001(x2+y2))2. f(0,1.25313)=0.292579 −100≤x,y≤100
Styblinski–Tang function: Styblinski-Tang function f(𝒙)=∑i=1nxi4−16xi2+5xi2. f(−2.903534,…,−2.903534⏟(n) times)=−39.16599n −5≤xi≤5, 1≤i≤n.


Test functions for multi-objective optimization problems

Name Plot Functions Constraints Search domain
Binh and Korn function: Binh and Korn function Minimize={f1(x,y)=4x2+4y2f2(x,y)=(x−5)2+(y−5)2 s.t.={g1(x,y)=(x−5)2+y2≤25g2(x,y)=(x−8)2+(y+3)2≥7.7 0≤x≤5, 0≤y≤3
Chakong and Haimes function: Chakong and Haimes function Minimize={f1(x,y)=2+(x−2)2+(y−1)2f2(x,y)=9x+(y−1)2 s.t.={g1(x,y)=x2+y2≤225g2(x,y)=x−3y+10≤0 −20≤x,y≤20
Fonseca and Fleming function: Fonseca and Fleming function Minimize={f1(𝒙)=1−exp⁡(−∑i=1n(xi−1n)2)f2(𝒙)=1−exp⁡(−∑i=1n(xi+1n)2) −4≤xi≤4, 1≤i≤n
Test function 4:[7] Test function 4.[7] Minimize={f1(x,y)=x2−yf2(x,y)=−0.5x−y−1 s.t.={g1(x,y)=6.5−x6−y≥0g2(x,y)=7.5−0.5x−y≥0g3(x,y)=30−5x−y≥0 −7≤x,y≤4
Kursawe function: Kursawe function Minimize={f1(𝒙)=∑i=12[−10exp⁡(−0.2xi2+xi+12)]f2(𝒙)=∑i=13[|xi|0.8+5sin⁡(xi3)] −5≤xi≤5, 1≤i≤3.
Schaffer function N. 1: Schaffer function N.1 Minimize={f1(x)=x2f2(x)=(x−2)2 −A≤x≤A. Values of A form 10 to 105 have been used successfully. Higher values of A increase the difficulty of the problem.
Schaffer function N. 2: Schaffer function N.2 Minimize={f1(x)={−x,if x≤1x−2,if 1<x≤34−x,if 3<x≤4x−4,if x>4f2(x)=(x−5)2 −5≤x≤10.
Poloni's two objective function: Poloni's two objective function Minimize={f1(x,y)=[1+(A1−B1(x,y))2+(A2−B2(x,y))2]f2(x,y)=(x+3)2+(y+1)2

where={A1=0.5sin⁡(1)−2cos⁡(1)+sin⁡(2)−1.5cos⁡(2)A2=1.5sin⁡(1)−cos⁡(1)+2sin⁡(2)−0.5cos⁡(2)B1(x,y)=0.5sin⁡(x)−2cos⁡(x)+sin⁡(y)−1.5cos⁡(y)B2(x,y)=1.5sin⁡(x)−cos⁡(x)+2sin⁡(y)−0.5cos⁡(y)

−π≤x,y≤π
Zitzler–Deb–Thiele's function N. 1: Zitzler-Deb-Thiele's function N.2 Minimize={f1(𝒙)=x1f2(𝒙)=g(𝒙)h(f1(𝒙),g(𝒙))g(𝒙)=1+929∑i=230xih(f1(𝒙),g(𝒙))=1−f1(𝒙)g(𝒙) 0≤xi≤1, 1≤i≤30.
Zitzler–Deb–Thiele's function N. 2: Zitzler-Deb-Thiele's function N.2 Minimize={f1(𝒙)=x1f2(𝒙)=g(𝒙)h(f1(𝒙),g(𝒙))g(𝒙)=1+929∑i=230xih(f1(𝒙),g(𝒙))=1−(f1(𝒙)g(𝒙))2 0≤xi≤1, 1≤i≤30.
Zitzler–Deb–Thiele's function N. 3: Zitzler-Deb-Thiele's function N.3 Minimize={f1(𝒙)=x1f2(𝒙)=g(𝒙)h(f1(𝒙),g(𝒙))g(𝒙)=1+929∑i=230xih(f1(𝒙),g(𝒙))=1−f1(𝒙)g(𝒙)−(f1(𝒙)g(𝒙))sin⁡(10πf1(𝒙)) 0≤xi≤1, 1≤i≤30.
Zitzler–Deb–Thiele's function N. 4: caption2 = Zitzler-Deb-Thiele's function N.4 Minimize={f1(𝒙)=x1f2(𝒙)=g(𝒙)h(f1(𝒙),g(𝒙))g(𝒙)=91+∑i=210(xi2−10cos⁡(4πxi))h(f1(𝒙),g(𝒙))=1−f1(𝒙)g(𝒙) 0≤x1≤1, −5≤xi≤5, 2≤i≤10
Zitzler–Deb–Thiele's function N. 6: Zitzler-Deb-Thiele's function N.6 Minimize={f1(𝒙)=1−exp⁡(−4x1)sin6(6πx1)f2(𝒙)=g(𝒙)h(f1(𝒙),g(𝒙))g(𝒙)=1+9[∑i=210xi9]0.25h(f1(𝒙),g(𝒙))=1−(f1(𝒙)g(𝒙))2 0≤xi≤1, 1≤i≤10.
Viennet function: Viennet function Minimize={f1(x,y)=0.5(x2+y2)+sin⁡(x2+y2)f2(x,y)=(3x−2y+4)28+(x−y+1)227+15f3(x,y)=1x2+y2+1−1.1exp⁡(−(x2+y2)) −3≤x,y≤3.
Osyczka and Kundu function: Osyczka and Kundu function Minimize={f1(𝒙)=−25(x1−2)2−(x2−2)2−(x3−1)2−(x4−4)2−(x5−1)2f2(𝒙)=∑i=16xi2 s.t.={g1(𝒙)=x1+x2−2≥0g2(𝒙)=6−x1−x2≥0g3(𝒙)=2−x2+x1≥0g4(𝒙)=2−x1+3x2≥0g5(𝒙)=4−(x3−3)2−x4≥0g6(𝒙)=(x5−3)2+x6−4≥0 0≤x1,x2,x6≤10, 1≤x3,x5≤5, 0≤x4≤6.
CTP1 function (2 variables):[5] CTP1 function (2 variables).[5] Minimize={f1(x,y)=xf2(x,y)=(1+y)exp⁡(−x1+y) s.t.={g1(x,y)=f2(x,y)0.858exp⁡(−0.541f1(x,y))≥1g1(x,y)=f2(x,y)0.728exp⁡(−0.295f1(x,y))≥1 0≤x,y≤1.
Constr-Ex problem:[5] Constr-Ex problem.[5] Minimize={f1(x,y)=xf2(x,y)=1+yx s.t.={g1(x,y)=y+9x≥6g1(x,y)=−y+9x≥1 0.1≤x≤1, 0≤y≤5


See also

References

  1. ↑ 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  2. ↑ 20 year-old Real Estate Agent Rusty from Saint-Paul, has hobbies and interests which includes monopoly, property developers in singapore and poker. Will soon undertake a contiki trip that may include going to the Lower Valley of the Omo.

    My blog: http://www.primaboinca.com/view_profile.php?userid=5889534
  3. ↑ Template:Cite web
  4. ↑ Template:Cite web
  5. ↑ 5.0 5.1 5.2 5.3 5.4 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. ↑ 7.0 7.1 7.2 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. ↑ Template:Cite web