Global Optimization Test

Global Optimization Test Problems. Many test problems are presented here in order to examine the performance of global optimization methods. The behavior of these test problems varies to cover most difficulties faced in the area of continuous global optimization. The presented problems are: Test Functions for Unconstrained Global Optimization. Test Problems for Constrained Global Optimization.

References in zbMATH (referenced in 20 articles )

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  1. Audet, Charles; Bigeon, Jean; Couderc, Romain: Combining cross-entropy and MADS methods for inequality constrained global optimization (2021)
  2. Jones, Donald R.; Martins, Joaquim R. R. A.: The DIRECT algorithm: 25 years later (2021)
  3. Rostamian, Mehdi; Kashani, Ali R.; Camp, Charles V.; Gandomi, Amir H.: Experimental comparison of constraint handling schemes in particle swarm optimization (2021)
  4. Stripinis, Linas; Žilinskas, Julius; Casado, Leocadio G.; Paulavičius, Remigijus: On \textttMATLABexperience in accelerating \textttDIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization (2021)
  5. Audet, Charles; Le Digabel, Sébastien; Tribes, Christophe: The mesh adaptive direct search algorithm for granular and discrete variables (2019)
  6. Sahiner, Ahmet; Ibrahem, Shehab A.: A new global optimization technique by auxiliary function method in a directional search (2019)
  7. Amaioua, Nadir; Audet, Charles; Conn, Andrew R.; Le Digabel, Sébastien: Efficient solution of quadratically constrained quadratic subproblems within the mesh adaptive direct search algorithm (2018)
  8. Audet, Charles; Tribes, Christophe: Mesh-based Nelder-Mead algorithm for inequality constrained optimization (2018)
  9. Paulavičius, Remigijus; Chiter, Lakhdar; Žilinskas, Julius: Global optimization based on bisection of rectangles, function values at diagonals, and a set of Lipschitz constants (2018)
  10. Stripinis, Linas; Paulavičius, Remigijus; Žilinskas, Julius: Improved scheme for selection of potentially optimal hyper-rectangles in \textttDIRECT (2018)
  11. Hladík, Milan: An extension of the (\alpha\mathrmBB)-type underestimation to linear parametric Hessian matrices (2016)
  12. Ahrari, Ali; Shariat-Panahi, Masoud: An improved evolution strategy with adaptive population size (2015)
  13. Bofill, Josep Maria; Quapp, Wolfgang; Bernuz, Efrem: Some remarks on the model of the extended gentlest ascent dynamics (2015)
  14. de-los-Cobos-Silva, Sergio Gerardo; Gutiérrez-Andrade, Miguel Ángel; Mora-Gutiérrez, Roman Anselmo; Lara-Velázquez, Pedro; Rincón-García, Eric Alfredo; Ponsich, Antonin: An efficient algorithm for unconstrained optimization (2015)
  15. Kocuk, Burak; Altınel, İ. Kuban; Aras, Necati: Approximating the objective function’s gradient using perceptrons for constrained minimization with application in drag reduction (2015)
  16. Liu, Qunfeng; Zeng, Jinping; Yang, Gang: MrDIRECT: a multilevel robust DIRECT algorithm for global optimization problems (2015)
  17. Penev, Kalin: Free search in multidimensional space (2014)
  18. Wei, Fei; Wang, Yuping; Lin, Hongwei: A new filled function method with two parameters for global optimization (2014)
  19. Hu, Chunping; Wang, Cuifang; Yan, Xuefeng: A self-adaptive differential evolution algorithm based on ant system with application to estimate kinetic parameters (2012)
  20. Li, Genzi; Aute, Vikrant; Azarm, Shapour: An accumulative error based adaptive design of experiments for offline metamodeling (2010) ioport