Test functions for optimization
Template:Short description 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 BΓ€ck,[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
Test functions for constrained optimization
| Name | Plot | Formula | Global minimum | Search domain |
|---|---|---|---|---|
| Rosenbrock function constrained to a disk[11] | Rosenbrock function constrained to a disk | ,
subjected to: |
, | |
| Mishra's Bird function - constrained[12][13] | Bird function (constrained) | ,
subjected to: |
, | |
| Townsend function (modified)[14] | Heart constrained multimodal function | ,
subjected to: where: t = Atan2(x,y) |
, | |
| Keane's bump function[15] | Keane's bump function | ,
subjected to: , and |
Test functions for multi-objective optimization
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| Name | Plot | Functions | Constraints | Search domain |
|---|---|---|---|---|
| Binh and Korn function:[5] | Binh and Korn function | , | ||
| Chankong and Haimes function:[16] | Chakong and Haimes function | |||
| FonsecaβFleming function:[17] | Fonseca and Fleming function | , | ||
| Test function 4:[6] | Test function 4.[6] | |||
| Kursawe function:[18] | Kursawe function | , . | ||
| Schaffer function N. 1:[19] | 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 |
|
||
| ZitzlerβDebβThiele's function N. 1:[20] | Zitzler-Deb-Thiele's function N.1 | , . | ||
| ZitzlerβDebβThiele's function N. 2:[20] | Zitzler-Deb-Thiele's function N.2 | , . | ||
| ZitzlerβDebβThiele's function N. 3:[20] | Zitzler-Deb-Thiele's function N.3 | , . | ||
| ZitzlerβDebβThiele's function N. 4:[20] | Zitzler-Deb-Thiele's function N.4 | , , | ||
| ZitzlerβDebβThiele's function N. 6:[20] | Zitzler-Deb-Thiele's function N.6 | , . | ||
| Osyczka and Kundu function:[21] | Osyczka and Kundu function | , , . | ||
| CTP1 function (2 variables):[4][22] | CTP1 function (2 variables).[4] | . | ||
| Constr-Ex problem:[4] | Constr-Ex problem.[4] | , | ||
| Viennet function: | Viennet function | . |
References
- ^ Page Module:Citation/CS1/styles.css has no content.BΓ€ck, Thomas (1995). Evolutionary algorithms in theory and practice : evolution strategies, evolutionary programming, genetic algorithms. Oxford: Oxford University Press. p. 328. ISBN 978-0-19-509971-3.
- ^ Page Module:Citation/CS1/styles.css has no content.Haupt, Randy L. Haupt, Sue Ellen (2004). Practical genetic algorithms with CD-Rom (2nd ed.). New York: J. Wiley. ISBN 978-0-471-45565-3.
{{cite book}}: CS1 maint: multiple names: authors list (link) - ^ Page Module:Citation/CS1/styles.css has no content.Oldenhuis, Rody. "Many test functions for global optimizers". Mathworks. Retrieved 1 November 2012.
- ^ a b c d e Deb, Kalyanmoy (2002) Multiobjective optimization using evolutionary algorithms (Repr. ed.). Chichester [u.a.]: Wiley. Template:Isbn.
- ^ a b 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. 176β182
- ^ a b c 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: https://www.iitk.ac.in/kangal/codes.shtml
- ^ Page Module:Citation/CS1/styles.css has no content.Ortiz, Gilberto A. "Multi-objective optimization using ES as Evolutionary Algorithm". Mathworks. Retrieved 1 November 2012.
- ^ Page Module:Citation/CS1/styles.css has no content.Whitley, Darrell; Rana, Soraya; Dzubera, John; Mathias, Keith E. (1996). "Evaluating evolutionary algorithms". Artificial Intelligence. 85 (1β2). Elsevier BV: 264. doi:10.1016/0004-3702(95)00124-7. ISSN 0004-3702.
- ^ Vanaret C. (2015) Hybridization of interval methods and evolutionary algorithms for solving difficult optimization problems. PhD thesis. Ecole Nationale de l'Aviation Civile. Institut National Polytechnique de Toulouse, France.
- ^ Page Module:Citation/CS1/styles.css has no content."Solve a Constrained Nonlinear Problem - MATLAB & Simulink". www.mathworks.com. Retrieved 2017-08-29.
- ^ Page Module:Citation/CS1/styles.css has no content."Bird Problem (Constrained) | Phoenix Integration". Archived from the original on 2016-12-29. Retrieved 2017-08-29.
{{cite web}}: CS1 maint: bot: original URL status unknown (link) - ^ Page Module:Citation/CS1/styles.css has no content.Mishra, Sudhanshu (2006). "Some new test functions for global optimization and performance of repulsive particle swarm method". MPRA Paper.
- ^ Page Module:Citation/CS1/styles.css has no content.Townsend, Alex (January 2014). "Constrained optimization in Chebfun". chebfun.org. Retrieved 2017-08-29.
- ^ Page Module:Citation/CS1/styles.css has no content.Mishra, Sudhanshu (5 May 2007). "Minimization of Keane's Bump Function by the Repulsive Particle Swarm and the Differential Evolution Methods". MPRA Paper. University Library of Munich, Germany.
- ^ Page Module:Citation/CS1/styles.css has no content.Chankong, Vira; Haimes, Yacov Y. (1983). Multiobjective decision making. Theory and methodology. North Holland. ISBN 0-444-00710-5.
- ^ Page Module:Citation/CS1/styles.css has no content.Fonseca, C. M.; Fleming, P. J. (1995). "An Overview of Evolutionary Algorithms in Multiobjective Optimization". Evol Comput. 3 (1): 1β16. CiteSeerX 10.1.1.50.7779. doi:10.1162/evco.1995.3.1.1. S2CID 8530790.
- ^ F. Kursawe, βA variant of evolution strategies for vector optimization,β in PPSN I, Vol 496 Lect Notes in Comput Sc. Springer-Verlag, 1991, pp. 193β197.
- ^ Page Module:Citation/CS1/styles.css has no content.Schaffer, J. David (1984). "Multiple Objective Optimization with Vector Evaluated Genetic Algorithms". In G.J.E Grefensette; J.J. Lawrence Erlbraum (eds.). Proceedings of the First International Conference on Genetic Algorithms. OCLC 20004572.
- ^ a b c d e Page Module:Citation/CS1/styles.css has no content.Deb, Kalyan; Thiele, L.; Laumanns, Marco; Zitzler, Eckart (2002). "Scalable multi-objective optimization test problems". Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600). Vol. 1. pp. 825β830. doi:10.1109/CEC.2002.1007032. ISBN 0-7803-7282-4. S2CID 61001583.
- ^ Page Module:Citation/CS1/styles.css has no content.Osyczka, A.; Kundu, S. (1 October 1995). "A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm". Structural Optimization. 10 (2): 94β99. doi:10.1007/BF01743536. ISSN 1615-1488. S2CID 123433499.
- ^ Page Module:Citation/CS1/styles.css has no content.Jimenez, F.; Gomez-Skarmeta, A. F.; Sanchez, G.; Deb, K. (May 2002). "An evolutionary algorithm for constrained multi-objective optimization". Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600). Vol. 2. pp. 1133β1138. doi:10.1109/CEC.2002.1004402. ISBN 0-7803-7282-4. S2CID 56563996.