Algorithm 829

(GKLS generator) Algorithm 829: Software for generation of classes of test functions with known local and global minima for global optimization. A procedure for generating non-differentiable, continuously differentiable, and twice continuously differentiable classes of test functions for multiextremal multidimensional box-constrained global optimization is presented. Each test class consists of 100 functions. Test functions are generated by defining a convex quadratic function systematically distorted by polynomials in order to introduce local minima. To determine a class, the user defines the following parameters: (i) problem dimension, (ii) number of local minima, (iii) value of the global minimum, (iv) radius of the attraction region of the global minimizer, (v) distance from the global minimizer to the vertex of the quadratic function. Then, all other necessary parameters are generated randomly for all 100 functions of the class. Full information about each test function including locations and values of all local minima is supplied to the user. Partial derivatives are also generated where possible. (Source: http://dl.acm.org/)

This software is also peer reviewed by journal TOMS.


References in zbMATH (referenced in 56 articles , 1 standard article )

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  1. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; Tušar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  2. Jones, Donald R.; Martins, Joaquim R. R. A.: The DIRECT algorithm: 25 years later (2021)
  3. Zheng, Cuicui; Calvin, James; Gotsman, Craig: A \textscdirect-type global optimization algorithm for image registration (2021)
  4. Gergel, Victor; Grishagin, Vladimir; Israfilov, Ruslan: Multiextremal optimization in feasible regions with computable boundaries on the base of the adaptive nested scheme (2020)
  5. Strongin, R. G.; Gergel, V. P.; Barkalov, K. A.: Adaptive global optimization based on a block-recursive dimensionality reduction scheme (2020)
  6. Strongin, Roman; Barkalov, Konstantin; Bevzuk, Semen: Acceleration of global search by implementing dual estimates for Lipschitz constant (2020)
  7. Žilinskas, Antanas; Gimbutienė, Gražina: A hybrid of Bayesian approach based global search with clustering aided local refinement (2019)
  8. Barkalov, Konstantin; Strongin, Roman: Solving a set of global optimization problems by the parallel technique with uniform convergence (2018)
  9. Candelieri, A.; Perego, R.; Archetti, F.: Bayesian optimization of pump operations in water distribution systems (2018)
  10. Gergel, Victor; Barkalov, Konstantin; Sysoyev, Alexander: Globalizer: a novel supercomputer software system for solving time-consuming global optimization problems (2018)
  11. Gergel, Victor; Kozinov, Evgeny: Efficient multicriterial optimization based on intensive reuse of search information (2018)
  12. Gimbutas, Albertas; Žilinskas, Antanas: An algorithm of simplicial Lipschitz optimization with the bi-criteria selection of simplices for the bi-section (2018)
  13. Grishagin, Vladimir; Israfilov, Ruslan; Sergeyev, Yaroslav: Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes (2018)
  14. Kvasov, Dmitri E.; Mukhametzhanov, Marat S.: Metaheuristic vs. deterministic global optimization algorithms: the univariate case (2018)
  15. Larson, Jeffrey; Wild, Stefan M.: Asynchronously parallel optimization solver for finding multiple minima (2018)
  16. Lera, Daniela; Sergeyev, Yaroslav D.: GOSH: derivative-free global optimization using multi-dimensional space-filling curves (2018)
  17. Strongin, R. G.; Gergel, V. P.; Barkalov, K. A.; Sysoyev, A. V.: Generalized parallel computational schemes for time-consuming global optimization (2018)
  18. Beiranvand, Vahid; Hare, Warren; Lucet, Yves: Best practices for comparing optimization algorithms (2017)
  19. Fok, Ricky; An, Aijun; Wang, Xiaogong: Geodesic and contour optimization using conformal mapping (2017)
  20. Liu, Qunfeng; Yang, Guang; Zhang, Zhongzhi; Zeng, Jinping: Improving the convergence rate of the DIRECT global optimization algorithm (2017)

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