Stochastic Global Optimization: A Review on the Occasion of 25 Years of Informatica

Žilinskas, Antanas

doi:10.15388/Informatica.2016.83

Safe Global Optimization

Antonio Candelieri, Dmitri E. Kvasov, Yaroslav D. Sergeyev

https://doi.org/10.1007/978-3-030-54621-2_765-1

Book: (2023), p. 1

Learning Enabled Constrained Black-Box Optimization

F. Archetti, A. Candelieri, B. G. Galuzzi, R. Perego

https://doi.org/10.1007/978-3-030-66515-9_1

Book: Springer Optimization and Its Applications (Black Box Optimization, Machine Learning, and No-Free Lunch Theorems) Volume 170 (2021), p. 1

Bayesian Optimization Based on Simulation Conditionally to Subvariety

Frédéric Dambreville

https://doi.org/10.1007/978-3-030-92666-3_11

Book: Lecture Notes in Networks and Systems (Modelling, Computation and Optimization in Information Systems and Management Sciences) Volume 363 (2022), p. 120

Simulation Conditionally to a Subvariety and Application to Bayesian Optimization: A Dichotomous Approach

Frédéric Dambreville

https://doi.org/10.1007/978-3-031-23149-0_10

Book: Lecture Notes in Networks and Systems (Simulation and Modeling Methodologies, Technologies and Applications) Volume 601 (2023), p. 178

From Global Optimization to Optimal Learning

Francesco Archetti, Antonio Candelieri

https://doi.org/10.1007/978-3-030-24494-1_2

Book: SpringerBriefs in Optimization (Bayesian Optimization and Data Science) (2019), p. 19

AIP Conference Proceedings

Antanas Žilinskas

https://doi.org/10.1063/1.4965307

Conference: AIP Conference Proceedings Volume 1776 (2016), p. 020001

AIP Conference Proceedings

Antanas Žilinskas

https://doi.org/10.1063/1.5089987

Conference: AIP Conference Proceedings Volume 2070 (2019), p. 020020

Lipschitz Expensive Global Optimization

Dmitri E. Kvasov, Yaroslav D. Sergeyev

https://doi.org/10.1007/978-3-030-54621-2_764-1

Book: (2023), p. 1

AIP Conference Proceedings

Antanas Žilinskas, Anatoly Zhigljavsky, Vladimir Nekrutkin, Vladimir Kornikov

https://doi.org/10.1063/1.5090010

Conference: AIP Conference Proceedings Volume 2070 (2019), p. 020043

A Partition Based Bayesian Multi-objective Optimization Algorithm

Antanas Žilinskas, Linas Litvinas

https://doi.org/10.1007/978-3-030-40616-5_50

Book: Lecture Notes in Computer Science (Numerical Computations: Theory and Algorithms) Volume 11974 (2020), p. 511

INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2021

Dmitri E. Kvasov, Daniela Lera, Yaroslav D. Sergeyev

https://doi.org/10.1063/5.0162069

Conference: INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2021 (INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2021) Volume 2849 (2023), p. 450033

2D/3D Shaped Radiation Patterns of Sunflower and Conformal Antenna Arrays Using Phase Synthesis

Saber Helmy Zainud-Deen, Doaa Mohammed Zaki Azzam, Hend Abd El-Azem Malhat

https://doi.org/10.1007/s11277-020-07602-w

Journal: Wireless Personal Communications Volume 115, Issue 1 (2020), p. 877

A Bayesian optimization approach to find Nash equilibria

Victor Picheny, Mickael Binois, Abderrahmane Habbal

https://doi.org/10.1007/s10898-018-0688-0

Journal: Journal of Global Optimization Volume 73, Issue 1 (2019), p. 171

A hybrid of Bayesian approach based global search with clustering aided local refinement

Antanas Žilinskas, Gražina Gimbutienė

https://doi.org/10.1016/j.cnsns.2019.104857

Journal: Communications in Nonlinear Science and Numerical Simulation Volume 78 (2019), p. 104857

A hybrid of the simplicial partition-based Bayesian global search with the local descent

Antanas Žilinskas, Linas Litvinas

https://doi.org/10.1007/s00500-020-05095-0

Journal: Soft Computing Volume 24, Issue 23 (2020), p. 17601

Aggregated GP-based Optimization for Contaminant Source Localization

Tipaluck Krityakierne, Duangkamon Baowan

https://doi.org/10.1016/j.orp.2020.100151

Journal: Operations Research Perspectives Volume 7 (2020), p. 100151

An Improved Butterfly Optimization Algorithm for Engineering Design Problems Using the Cross-Entropy Method

Guocheng Li, Fei Shuang, Pan Zhao, Chengyi Le

https://doi.org/10.3390/sym11081049

Journal: Symmetry Volume 11, Issue 8 (2019), p. 1049

Bi-objective decision making in global optimization based on statistical models

Antanas Žilinskas, James Calvin

https://doi.org/10.1007/s10898-018-0622-5

Journal: Journal of Global Optimization Volume 74, Issue 4 (2019), p. 599

Computational Diagnostics of Jacobi Unstable Dynamical Systems with the Use of Hybrid Algorithms of Global Optimization

P.M. Shkapov, A.V. Sulimov, V.D. Sulimov

https://doi.org/10.18698/1812-3368-2021-4-40-56

Journal: Herald of the Bauman Moscow State Technical University. Series Natural Sciences Issue 4 (97) (2021), p. 40

Constrained optimization of black-box stochastic systems using a novel feasibility enhanced Kriging-based method

Zilong Wang, Marianthi Ierapetritou

https://doi.org/10.1016/j.compchemeng.2018.07.016

Journal: Computers & Chemical Engineering Volume 118 (2018), p. 210

Finding optimal points for expensive functions using adaptive RBF-based surrogate model via uncertainty quantification

Ray-Bing Chen, Yuan Wang, C. F. Jeff Wu

https://doi.org/10.1007/s10898-020-00916-w

Journal: Journal of Global Optimization Volume 77, Issue 4 (2020), p. 919

Globally-biased BIRECT algorithm with local accelerators for expensive global optimization

Remigijus Paulavičius, Yaroslav D. Sergeyev, Dmitri E. Kvasov, Julius Žilinskas

https://doi.org/10.1016/j.eswa.2019.113052

Journal: Expert Systems with Applications Volume 144 (2020), p. 113052

Monitoring the Area Change in the Three Gorges Reservoir Riparian Zone Based on Genetic Algorithm Optimized Machine Learning Algorithms and Sentinel-1 Data

Baisheng Xu, Wei Wu, Haohui Ye, Xinrong Li, Hongbin Liu

https://doi.org/10.3390/rs15235456

Journal: Remote Sensing Volume 15, Issue 23 (2023), p. 5456

Non-Stationary Stochastic Global Optimization Algorithms

Jonatan Gomez, Andres Rivera

https://doi.org/10.3390/a15100362

Journal: Algorithms Volume 15, Issue 10 (2022), p. 362

Novel local tuning techniques for speeding up one-dimensional algorithms in expensive global optimization using Lipschitz derivatives

Yaroslav D. Sergeyev, Maria Chiara Nasso, Marat S. Mukhametzhanov, Dmitri E. Kvasov

https://doi.org/10.1016/j.cam.2020.113134

Journal: Journal of Computational and Applied Mathematics Volume 383 (2021), p. 113134

On convergence rate of a rectangular partition based global optimization algorithm

James Calvin, Gražina Gimbutienė, William O. Phillips, Antanas Žilinskas

https://doi.org/10.1007/s10898-018-0636-z

Journal: Journal of Global Optimization Volume 71, Issue 1 (2018), p. 165

On the convergence rate of the quasi – Monte Carlo method of search for extremum

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1352/1/012051

Journal: Journal of Physics: Conference Series Volume 1352, Issue 1 (2019), p. 012051

On the program implementation of a Markov homogeneous monotonous random search algorithm of an extremum

A S Tikhomirov

https://doi.org/10.1088/1757-899X/441/1/012055

Journal: IOP Conference Series: Materials Science and Engineering Volume 441 (2018), p. 012055

On the program implementation of a Markov homogeneous random search algorithm of an extremum with Ingber’s distribution

A S Tikhomirov, N Yu Kropacheva

https://doi.org/10.1088/1742-6596/1352/1/012055

Journal: Journal of Physics: Conference Series Volume 1352, Issue 1 (2019), p. 012055

On the program implementation of a Markov homogeneous random search algorithm of an extremum with normal distributions

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1352/1/012052

Journal: Journal of Physics: Conference Series Volume 1352, Issue 1 (2019), p. 012052

On the program implementation of a Markov inhomogeneous random search algorithm with normal distributions

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1352/1/012053

Journal: Journal of Physics: Conference Series Volume 1352, Issue 1 (2019), p. 012053

On the program implementation of a simple Markov homogeneous random search algorithm of an extremum

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1658/1/012058

Journal: Journal of Physics: Conference Series Volume 1658, Issue 1 (2020), p. 012058

On the program implementation of a simple Markov homogeneous random search algorithm of an extremum with Cauchy distributions

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1658/1/012059

Journal: Journal of Physics: Conference Series Volume 1658, Issue 1 (2020), p. 012059

On the program implementation of a simple Markov homogeneous random search algorithm of an extremum with normal distributions

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1658/1/012060

Journal: Journal of Physics: Conference Series Volume 1658, Issue 1 (2020), p. 012060

On the program implementation of a simple Markov homogeneous random search algorithm of an extremum with triangular distributions

A S Tikhomirov

https://doi.org/10.1088/1742-6596/1658/1/012061

Journal: Journal of Physics: Conference Series Volume 1658, Issue 1 (2020), p. 012061

On the search of the shape parameter in radial basis functions using univariate global optimization methods

R. Cavoretto, A. De Rossi, M. S. Mukhametzhanov, Ya. D. Sergeyev

https://doi.org/10.1007/s10898-019-00853-3

Journal: Journal of Global Optimization Volume 79, Issue 2 (2021), p. 305

Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation

Usama Khaled, Ali M. Eltamaly, Abderrahmane Beroual

https://doi.org/10.3390/en10071013

Journal: Energies Volume 10, Issue 7 (2017), p. 1013

Safe global optimization of expensive noisy black-box functions in the $$\delta $$-Lipschitz framework

Yaroslav D. Sergeyev, Antonio Candelieri, Dmitri E. Kvasov, Riccardo Perego

https://doi.org/10.1007/s00500-020-05030-3

Journal: Soft Computing Volume 24, Issue 23 (2020), p. 17715

Selection of a covariance function for a Gaussian random field aimed for modeling global optimization problems

Anatoly Zhigljavsky, Antanas Žilinskas

https://doi.org/10.1007/s11590-018-1372-5

Journal: Optimization Letters Volume 13, Issue 2 (2019), p. 249

Spark implementation of the enhanced Scatter Search metaheuristic: Methodology and assessment

Xoán C. Pardo, Pablo Argüeso-Alejandro, Patricia González, Julio R. Banga, Ramón Doallo

https://doi.org/10.1016/j.swevo.2020.100748

Journal: Swarm and Evolutionary Computation Volume 59 (2020), p. 100748

Surrogate-based feasibility analysis for black-box stochastic simulations with heteroscedastic noise

Zilong Wang, Marianthi Ierapetritou

https://doi.org/10.1007/s10898-018-0615-4

Journal: Journal of Global Optimization Volume 71, Issue 4 (2018), p. 957

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