- Daniela Lera, Yaroslav D. Sergeyev, “GOSH: derivative-free global optimization using multi-dimensional space-filling curves”, J Glob Optim, 71, № 1, 2018, 193
- 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
- Erik Cuevas, Fernando Fausto, Adrián González, 160, New Advancements in Swarm Algorithms: Operators and Applications, 2020, 161
- 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
- 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
- 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
- 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
- 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
- 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
- Jorge Gálvez, Erik Cuevas, Krishna Gopal Dhal, “A Competitive Memory Paradigm for Multimodal Optimization Driven by Clustering and Chaos”, Mathematics, 8, № 6, 2020, 934