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Test functions for optimization : ウィキペディア英語版 | 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, Haupt et. al. and from Rody Oldenhuis software. 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. The test functions used to evaluate the algorithms for MOP were taken from Deb,〔Deb, Kalyanmoy (2002) Multiobjective optimization using evolutionary algorithms (Repr. ed.). Chichester (): Wiley. ISBN 0-471-87339-X.〕 Binh et. al.〔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〕 and Binh.〔Binh T. (1999) A multiobjective evolutionary algorithm. The study cases. Technical report. Institute for Automation and Communication. Barleben, Germany〕 You can download the software developed by Deb,〔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〕 which implements the NSGA-II procedure with GAs, or the program posted on Internet, 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==
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|| || |- | Sphere function || || || || , |- | Rosenbrock function || || || || , |- | Beale's function || ||
|| || |- | Goldstein–Price function: || ||
|| || |- | Booth's function: || || || || |- | Bukin function N.6: || || || || , |- | Matyas function: || || || || |- | Lévi function N.13: || ||
|| || |- | Three-hump camel function: || || || || |- | Easom function: || || || || |- | Cross-in-tray function: || || || || |- | Eggholder function: || || || || |- | Hölder table function: || || || || |- | McCormick function: || || || || , |- | Schaffer function N. 2: || || || || |- | Schaffer function N. 4: || || || || |- | Styblinski–Tang function: || || || , . |- | Simionescu function: || || ,
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