59 citations to 10.1016/j.apnum.2009.10.004 (Crossref Cited-By Service)
  1. Daniela Lera, Yaroslav D. Sergeyev, “GOSH: derivative-free global optimization using multi-dimensional space-filling curves”, J Glob Optim, 71, № 1, 2018, 193  crossref
  2. Erik Cuevas, Alonso Echavarría, Marte A. Ramírez-Ortegón, “An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation”, Appl Intell, 40, № 2, 2014, 256  crossref
  3. Erik Cuevas, Fernando Fausto, Adrián González, 160, New Advancements in Swarm Algorithms: Operators and Applications, 2020, 161  crossref
  4. Vladimir Grishagin, Ruslan Israfilov, Yaroslav Sergeyev, “Convergence conditions and numerical comparison of global optimization methods based on dimensionality reduction schemes”, Applied Mathematics and Computation, 318, 2018, 270  crossref
  5. Yaroslav D. Sergeyev, Antonio Candelieri, Dmitri E. Kvasov, Riccardo Perego, “Safe global optimization of expensive noisy black-box functions in the $\delta $-Lipschitz framework”, Soft Comput, 24, № 23, 2020, 17715  crossref
  6. Akshay Seshadri, Felix Leditzky, Vikesh Siddhu, Graeme Smith, “On the Separation of Correlation-Assisted Sum Capacities of Multiple Access Channels”, IEEE Trans. Inform. Theory, 69, № 9, 2023, 5805  crossref
  7. R. G. Strongin, V. P. Gergel, K. A. Barkalov, A. V. Sysoyev, “Generalized Parallel Computational Schemes for Time-Consuming Global Optimization”, Lobachevskii J Math, 39, № 4, 2018, 576  crossref
  8. Yaroslav D. Sergeyev, Maria Chiara Nasso, Daniela Lera, “Numerical methods using two different approximations of space-filling curves for black-box global optimization”, J Glob Optim, 88, № 3, 2024, 707  crossref
  9. Daniela Lera, Yaroslav D. Sergeyev, “Deterministic global optimization using space-filling curves and multiple estimates of Lipschitz and Hölder constants”, Communications in Nonlinear Science and Numerical Simulation, 23, № 1-3, 2015, 328  crossref
  10. Jorge Gálvez, Erik Cuevas, Krishna Gopal Dhal, “A Competitive Memory Paradigm for Multimodal Optimization Driven by Clustering and Chaos”, Mathematics, 8, № 6, 2020, 934  crossref
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