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Journal articles on the topic 'Stochastical optimization'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Ermakov, Sergei M., and Vyacheslav B. Melas. "Stochastical computation methods and experimental designing." Vestnik of Saint Petersburg University. Mathematics. Mechanics. Astronomy 10, no. 2 (2023): 187–99. http://dx.doi.org/10.21638/spbu01.2023.201.

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This paper contains a brief review of the most important results obtained by the staff of the department of statistical modeling. Results include mathematical justification of computer simulation of randomness, stochastic methods of solving equations, stochastic optimization, study of stochastic stability and parallelism of Monte-Carlo algorithms. In the area of experiment planning, special attention is paid to regression experiment under nonlinear parameterization. The list of references mainly includes monographs written by members of the department. The exceptions are some articles with res

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Hussen, Ayad Kasem. "SPEED ESTIMATION USING EXTENDED KALMAN FILTER TECHNIQUE." Tikrit Journal of Engineering Sciences 12, no. 1 (2005): 115–39. http://dx.doi.org/10.25130/tjes.12.1.09.

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This paper presents a state estimation technique for speed sensorless field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations using MATLAB package VER.6.3.A stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for eithe

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Hussen, Ayad Kasem. "Speed Estimation Using Extended Kalman Filter Technique." Tikrit Journal of Engineering Sciences 12, no. 3 (2005): 115–39. http://dx.doi.org/10.25130/tjes.12.3.06.

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This paper presents a state estimation technique for speed senseless field oriented control of induction motors. The theoretical basis of each algorithm is explained in detail and its performance is tested with simulations using MATLAB package VER.6.3. A stochastical nonlinear state estimator, Extended Kalman Filter (EKF) is presented. The motor model designed for EKF application involves rotor speed, dq-axis stator currents. Thus, using this observer the rotor speed and rotor fluxes are estimated simultaneously. Different from the widely accepted use of EKF, in which it is optimized for eithe

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Lapinskaitė, Indrė, and Aleksandras Rutkauskas. "The Optimization of Marketing Costs' Structure as a Prerequisite for Business Sustainable Development." Business: Theory and Practice 14, no. (1) (2013): 74–82. https://doi.org/10.3846/btp.2013.09.

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The idea of quantitative measurement of sustainability is revealed in this article. This idea is based on outlining the possibility of the event or the process, taking into account both possibility of efficiency and reliability of effectiveness. For the selection of appropriate compositions of efficiency and reliability for a particular subject an adequate idea of the utility function was used. Markovitz random field makes it possible to optimize the allocation of existing resources for the marketing costs between the components of marketing structure. Technically, the task is formulated as fo

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Riaz, Muhammad, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem, and Lucian Mihet-Popa. "Probabilistic Optimization Techniques in Smart Power System." Energies 15, no. 3 (2022): 825. http://dx.doi.org/10.3390/en15030825.

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Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to op

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Azodi, Peyman, Peyman Setoodeh, Alireza Khayatian, and Elham Jamalinia. "Stochastic boundedness of state trajectories of stable LTI systems in the presence of non-vanishing stochastic perturbation." IMA Journal of Mathematical Control and Information 37, no. 3 (2019): 718–29. http://dx.doi.org/10.1093/imamci/dnz023.

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Abstract This paper studies stochastic boundedness of trajectories of a non-vanishing stochastically perturbed stable linear time-invariant system. First, two definitions on stochastic boundedness are presented, then, the boundedness is analyzed via Lyapunov theory. A theorem is proposed, which shows that under a condition on the Lipchitz constant of the perturbation kernel, the trajectories remain stochastically bounded, and the bounds are calculated. Also, the limiting behaviour of the trajectories is studied. At the end, an illustrative example is presented, which shows the effectiveness of

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Sihotang, Hengki Tamando, Syahril Efendi, Muhammad Zarlis, and Herman Mawengkang. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497–504. http://dx.doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical examp

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Hengki, Tamando Sihotang, Efendi Syahril, Zarlis Muhammad, and Mawengkang Herman. "Data driven approach for stochastic data envelopment analysis." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1497~1504. https://doi.org/10.11591/eei.v11i3.3660.

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Decision making based on data driven deals with a large amount of data will evaluate the process's effectiveness. Evaluate effectiveness in this paper is measure of performance efficiency of data envelopment analysis (DEA) method in this study is the approach with uncertainty problems. This study proposed a new method called the robust stochastic DEA (RSDEA) to approach performance efficiency in tackling uncertainty problems (i.e., stochastic and robust optimization). The RSDEA method develops to combine the stochastics DEA (SDEA) formulation method and Robust Optimization. The numerical e

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Ashour, Marwan Abdul Hameed, Alyaa Abdulameer Ahmed, and Ammar Sh Ahmed. "Optimization algorithms for transportation problems with stochastic demand." Periodicals of Engineering and Natural Sciences (PEN) 10, no. 3 (2022): 172–79. https://doi.org/10.21533/pen.v10.i3.654.

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The purpose of this paper is to solve the stochastic demand for the unbalanced transport problem using heuristic algorithms to obtain the optimum solution, by minimizing the costs of transporting the gasoline product for the Oil Products Distribution Company of the Iraqi Ministry of Oil. The most important conclusions that were reached are the results prove the possibility of solving the random transportation problem when the demand is uncertain by the stochastic programming model. The most obvious finding to emerge from this work is that the genetic algorithm was able to address the problems

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K. Sivaselvan, K. Sivaselvan, and C. Vijayalakshmi C. Vijayalakshmi. "Stochastic Control Optimization Technique on Multi-Server Markovian Queueing System." Indian Journal of Applied Research 3, no. 7 (2011): 375–78. http://dx.doi.org/10.15373/2249555x/july2013/115.

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Kim, Minyoung, and Timothy Hospedales. "A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 17913–20. https://doi.org/10.1609/aaai.v39i17.33970.

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We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning, including hyperparameter optimization, loss function learning, few-shot learning and more. These problems are often formalized as Bi-Level Optimizations (BLO). We introduce a novel perspective by turning a given BLO problem into a stochastic optimization, where the inner loss function becomes a smooth probability distribution, and the outer loss becomes an expected loss over the inner distribution. To solve this stochastic optimization, we adopt Stochastic Gradient Langevin Dynamics (SGLD) MC

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Sumata, H., F. Kauker, R. Gerdes, C. Köberle, and M. Karcher. "A comparison between gradient descent and stochastic approaches for parameter optimization of a sea ice model." Ocean Science 9, no. 4 (2013): 609–30. http://dx.doi.org/10.5194/os-9-609-2013.

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Abstract. Two types of optimization methods were applied to a parameter optimization problem in a coupled ocean–sea ice model of the Arctic, and applicability and efficiency of the respective methods were examined. One optimization utilizes a finite difference (FD) method based on a traditional gradient descent approach, while the other adopts a micro-genetic algorithm (μGA) as an example of a stochastic approach. The optimizations were performed by minimizing a cost function composed of model–data misfit of ice concentration, ice drift velocity and ice thickness. A series of optimizations wer

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Petridis, Konstantinos, Prasanta Kumar Dey, Amit K. Chattopadhyay, Paraskevi Boufounou, Kanellos Toudas, and Chrisovalantis Malesios. "A Stochastically Optimized Two-Echelon Supply Chain Model: An Entropy Approach for Operational Risk Assessment." Entropy 25, no. 9 (2023): 1245. http://dx.doi.org/10.3390/e25091245.

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Minimizing a company’s operational risk by optimizing the performance of the manufacturing and distribution supply chain is a complex task that involves multiple elements, each with their own supply line constraints. Traditional approaches to optimization often assume determinism as the underlying principle. However, this paper, adopting an entropy approach, emphasizes the significance of subjective and objective uncertainty in achieving optimized decisions by incorporating stochastic fluctuations into the supply chain structure. Stochasticity, representing randomness, quantifies the level of

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Chimal-Eguia, Juan C., Julio C. Rangel-Reyes, and Ricardo T. Paez-Hernandez. "Improving Convergence in Therapy Scheduling Optimization: A Simulation Study." Mathematics 8, no. 12 (2020): 2114. http://dx.doi.org/10.3390/math8122114.

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The infusion times and drug quantities are two primary variables to optimize when designing a therapeutic schedule. In this work, we test and analyze several extensions to the gradient descent equations in an optimal control algorithm conceived for therapy scheduling optimization. The goal is to provide insights into the best strategies to follow in terms of convergence speed when implementing our method in models for dendritic cell immunotherapy. The method gives a pulsed-like control that models a series of bolus injections and aims to minimize a cost a function, which minimizes tumor size a

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Azanaw, Mr Girmay Mengesha. "Advanced Computational Methods for Simulating and Optimizing Stochastic Fracture: A Systematic Literature Review." International Journal of Emerging Science and Engineering 12, no. 10 (2024): 1–6. http://dx.doi.org/10.35940/ijese.h2579.12100924.

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Stochastic fracture processes, pervasive in diverse natural and engineered systems, pose intricate challenges for accurate simulation and optimization. This systematic literature review surveys the landscape of advanced computational methodologies to unravel and optimize stochastic fracture phenomena. Grounded in multidisciplinary perspectives spanning engineering, physics, and applied mathematics, the review navigates through the intricacies of simulation techniques and optimizations methods. From Finite Element Method (FEM) to Molecular Dynamics (MD) simulations, the review delineates the ev

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Mroua, Mourad, Fathi Abid, and Wing Keung Wong. "Optimal diversification, stochastic dominance, and sampling error." American Journal of Business 32, no. 1 (2017): 58–79. http://dx.doi.org/10.1108/ajb-04-2015-0014.

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Purpose The purpose of this paper is to contribute to the literature in three ways: first, the authors investigate the impact of the sampling errors on optimal portfolio weights and on financial investment decision. Second, the authors advance a comparative analysis between various domestic and international diversification strategies to define a stochastic optimal choice. Third, the authors propose a new methodology combining the re-sampling method, stochastic optimization algorithm, and nonparametric stochastic dominance (SD) approach to analyze a stochastic optimal portfolio choice for risk

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Putek, Piotr, Roland Pulch, Andreas Bartel, Maten E. Jan W. ter, Michael Günther, and Konstanty M. Gawrylczyk. "Shape and topology optimization of a permanent-magnet machine under uncertainties." Journal of Mathematics in Industry 6, no. 1 (2016): 11. https://doi.org/10.1186/s13362-016-0032-6.

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Our ultimate goal is a topology optimization for a permanent-magnet (PM) machine, while including material uncertainties. The uncertainties in the output data are, e.g., due to measurement errors in the non-/linear material laws. In the resulting stochastic forward problem, these uncertainties are stochastically modeled by random fields. The solution of the underlying PDE, which describes magnetostatics, is represented using the generalized polynomial chaos expansion. As crucial ingredient we exploit the stochastic collocation method (SCM). Eventually, this leads to a random-dependent bi-objec

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Yu, Chunxue, Xinan Yin, Zhifeng Yang, and Zhi Dang. "Sustainable Water Resource Management of Regulated Rivers under Uncertain Inflow Conditions Using a Noisy Genetic Algorithm." International Journal of Environmental Research and Public Health 16, no. 5 (2019): 868. http://dx.doi.org/10.3390/ijerph16050868.

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Ecofriendly reservoir operation is an important tool for sustainable water resource management in regulated rivers. Optimization of reservoir operation is potentially affected by the stochastic characteristics of inflows. However, inflow stochastics are not widely incorporated in ecofriendly reservoir operation optimization. The reasons might be that computational cost and unsatisfactory performance are two key issues for reservoir operation under uncertainty inflows, since traditional simulation methods are usually needed to evaluate over many realizations and the results vary between differe

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19

LIUM, ARNT-GUNNAR, TEODOR GABRIEL CRAINIC, and STEIN W. WALLACE. "CORRELATIONS IN STOCHASTIC PROGRAMMING: A CASE FROM STOCHASTIC SERVICE NETWORK DESIGN." Asia-Pacific Journal of Operational Research 24, no. 02 (2007): 161–79. http://dx.doi.org/10.1142/s0217595907001206.

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Deterministic models, even if used repeatedly, will not capture the essence of planning in an uncertain world. Flexibility and robustness can only be properly valued in models that use stochastics explicitly, such as stochastic optimization models. However, it may also be very important to capture how the random phenomena are related to one another. In this article we show how the solution to a stochastic service network design model depends heavily on the correlation structure among the random demands. The major goal of this paper is to discuss why this happens, and to provide insights into t

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Gauchon, Romain, and Karim Barigou. "Expected Utility Optimization with Convolutional Stochastically Ordered Returns." Risks 12, no. 6 (2024): 95. http://dx.doi.org/10.3390/risks12060095.

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Expected utility theory is critical for modeling rational decision making under uncertainty, guiding economic agents as they seek to optimize outcomes. Traditional methods often require restrictive assumptions about underlying stochastic processes, limiting their applicability. This paper expands the theoretical framework by considering investment returns modeled by a stochastically ordered family of random variables under the convolution order, including Poisson, Gamma, and exponential distributions. Utilizing fractional calculus, we derive explicit, closed-form expressions for the derivative

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21

Mr., Girmay Mengesha Azanaw. "Advanced Computational Methods for Simulating and Optimizing Stochastic Fracture: A Systematic Literature Review." International Journal of Emerging Science and Engineering (IJESE) 12, no. 10 (2024): 1–6. https://doi.org/10.35940/ijese.H2579.12100924.

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<strong>Abstract:</strong> Stochastic fracture processes, pervasive in diverse natural and engineered systems, pose intricate challenges for accurate simulation and optimization. This systematic literature review surveys the landscape of advanced computational methodologies to unravel and optimize stochastic fracture phenomena. Grounded in multidisciplinary perspectives spanning engineering, physics, and applied mathematics, the review navigates through the intricacies of simulation techniques and optimizations methods. From Finite Element Method (FEM) to Molecular Dynamics (MD) simulations, t

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Bene, László, Miklós Bagdány, and László Damjanovich. "T-cell Receptor Is a Threshold Detector: Sub- and Supra-Threshold Stochastic Resonance in TCR-MHC Clusters on the Cell Surface." Entropy 24, no. 3 (2022): 389. http://dx.doi.org/10.3390/e24030389.

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Stochastic resonance in clusters of major histocompatibility molecules is extended by a more detailed description of adaptive thresholding and by applying the notion of suprathreshold stochastic resonance as a stochastically quantizing encoder of transmembrane signaling downstream of major histocompatibility molecules and T-cell receptors on the side of presenting and recognizing cells, respectively. The adaptive nature of thresholding is partly explained by a mirroring of the noncognate–cognate dichotomy shown by the T-cell receptor structure and the kinetic-segregation model of the onset of

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Maureen, Nalubowa S., Kizito Mubiru Paul, Ochola Jerry, and Namango Saul. "Multi-Objective Optimization of Manufacturing Lot Size Under Stochastic Demand." International Journal of Current Science Research and Review 05, no. 02 (2022): 306–18. https://doi.org/10.5281/zenodo.5947176.

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Abstract : In many manufacturing problems, multi-objective optimizations are representative models, as objectives are considered a conflict with one another. In real-life applications, optimizing a specific solution concerning one objective may end up in unacceptable results concerning the other objectives. Many Manufacturing companies operate under uncertainties and this affects the system performance. Stochastic product demand is one of the challenges faced by manufacturing companies and often affects the manufacturing system’s performance and decision-making. Making the proper decisio

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Fukushima-Kimura, Bruno Hideki, Yoshinori Kamijima, Kazushi Kawamura, and Akira Sakai. "Stochastic Optimization." Transactions of the Institute of Systems, Control and Information Engineers 36, no. 1 (2023): 9–16. http://dx.doi.org/10.5687/iscie.36.9.

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Faris, Ahmad, Ismail Musirin, Shahrizal Jelani, Saiful Amri Ismail, Mohd Helmi Mansor, and A. V. Senthil Kumar. "Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2253–60. http://dx.doi.org/10.11591/eei.v9i6.2505.

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Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and syst

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Ahmad, Faris, Musirin Ismail, Jelani Shahrizal, Amri Ismail Saiful, Helmi Mansor Mohd, and V. Senthil Kumar A. "Tap changer optimisation using embedded differential evolutionary programming technique for loss control in power system." Bulletin of Electrical Engineering and Informatics 9, no. 6 (2020): 2253–60. https://doi.org/10.11591/eei.v9i6.2505.

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Over-compensation and under-compensation phenomena are two undesirable results in power system compensation. This will be not a good option in power system planning and operation. The non-optimal values of the compensating parameters subjected to a power system have contributed to these phenomena. Thus, a reliable optimization technique is mandatory to alleviate this issue. This paper presents a stochastic optimization technique used to fix the power loss control in a high demand power system due to the load increase, which causes the voltage decay problems leading to current increase and syst

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Grunbacher, Sophie, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, and Radu Grosu. "On the Verification of Neural ODEs with Stochastic Guarantees." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11525–35. http://dx.doi.org/10.1609/aaai.v35i13.17372.

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We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states over a given time-horizon), and provide stochastic guarantees in the form of confidence intervals for the Reachtube bounds. SLR inherently avoids the infamous wrapping effect (accumulation of over-approximation errors) by performing local optimization steps to

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Göçgün, Yasin. "Performance comparison of approximate dynamic programming techniques for dynamic stochastic scheduling." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 11, no. 2 (2021): 178–85. http://dx.doi.org/10.11121/ijocta.01.2021.00987.

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This paper focuses on the performance comparison of several approximate dynamic programming (ADP) techniques. In particular, we evaluate three ADP techniques through a class of dynamic stochastic scheduling problems: Lagrangian-based ADP, linear programming-based ADP, and direct search-based ADP. We uniquely implement the direct search-based ADP through basis functions that differ from those used in the relevant literature. The class of scheduling problems has the property that jobs arriving dynamically and stochastically must be scheduled to days in advance. Numerical results reveal that the

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Liu, Jin, Xiaokang Pan, Junwen Duan, Hong-Dong Li, Youqi Li, and Zhe Qu. "Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13927–35. http://dx.doi.org/10.1609/aaai.v38i12.29300.

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This paper delves into the realm of stochastic optimization for compositional minimax optimization—a pivotal challenge across various machine learning domains, including deep AUC and reinforcement learning policy evaluation. Despite its significance, the problem of compositional minimax optimization is still under-explored. Adding to the complexity, current methods of compositional minimax optimization are plagued by sub-optimal complexities or heavy reliance on sizable batch sizes. To respond to these constraints, this paper introduces a novel method, called Nested STOchastic Recursive Moment

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CASTANIER, BRUNO, ANTOINE GRALL, and CHRISTOPHE BÉRENGUER. "A STOCHASTIC MODEL FOR HYBRID MAINTENANCE POLICIES EVALUATION AND OPTIMIZATION." International Journal of Reliability, Quality and Safety Engineering 08, no. 03 (2001): 233–48. http://dx.doi.org/10.1142/s0218539301000499.

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We propose a hybrid maintenance policy which combines periodic (age-based or time-based) regulation-based inspections with aperiodic condition-based inspection/replacements for a stochastically and gradually deteriorating system. The stationary laws of the deterioration state of the maintained system are derived in order to evaluate the long-run average running cost on an infinite span generated by the proposed combined policy. A computable expression of the average cost is established using the regenerative or semi-regenerative properties of the stochastic process describing the maintained sy

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Park, Sungjin, and Sangkyun Kim. "K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data." Applied Sciences 12, no. 23 (2022): 12451. http://dx.doi.org/10.3390/app122312451.

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Clustering is widely used in client-facing businesses to categorize their customer base and deliver personalized services. This study proposes an algorithm to stochastically search for an optimum solution based on the outcomes of a data clustering process. Fundamentally, the aforementioned goal is achieved using a result-based stochastic search algorithm. Hence, shortcomings of existing stochastic search algorithms are identified, and the k-means-initiated rapid biogeography-based silhouette optimization (K-RBBSO) algorithm is proposed to overcome them. The proposed algorithm is validated by c

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Li, Tiankai, Baobin Wang, Chaoquan Peng, and Hong Yin. "Stochastic Gradient Descent for Kernel-Based Maximum Correntropy Criterion." Entropy 26, no. 12 (2024): 1104. https://doi.org/10.3390/e26121104.

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Maximum correntropy criterion (MCC) has been an important method in machine learning and signal processing communities since it was successfully applied in various non-Gaussian noise scenarios. In comparison with the classical least squares method (LS), which takes only the second-order moment of models into consideration and belongs to the convex optimization problem, MCC captures the high-order information of models that play crucial roles in robust learning, which is usually accompanied by solving the non-convexity optimization problems. As we know, the theoretical research on convex optimi

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Kumar, Mithlesh, and Gargishankar Verma. "MLP and RNN Based Intrusion Detection System Using Machine Learning with Stochastic Optimization." International Journal of Science and Research (IJSR) 10, no. 12 (2021): 158–61. https://doi.org/10.21275/mr211201200907.

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Yan, Di, and H. Mukai. "Stochastic Discrete Optimization." SIAM Journal on Control and Optimization 30, no. 3 (1992): 594–612. http://dx.doi.org/10.1137/0330034.

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ARCISZEWSKI, TOMASZ. "STOCHASTIC FORM OPTIMIZATION." Engineering Optimization 13, no. 1 (1988): 17–33. http://dx.doi.org/10.1080/03052158808940944.

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Schkufza, Eric, Rahul Sharma, and Alex Aiken. "Stochastic program optimization." Communications of the ACM 59, no. 2 (2016): 114–22. http://dx.doi.org/10.1145/2863701.

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Apolloni, B., C. Carvalho, and D. de Falco. "Quantum stochastic optimization." Stochastic Processes and their Applications 33, no. 2 (1989): 233–44. http://dx.doi.org/10.1016/0304-4149(89)90040-9.

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Nie, Jiawang, Liu Yang, and Suhan Zhong. "Stochastic polynomial optimization." Optimization Methods and Software 35, no. 2 (2019): 329–47. http://dx.doi.org/10.1080/10556788.2019.1649672.

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Curtis, Frank E., and Katya Scheinberg. "Adaptive Stochastic Optimization: A Framework for Analyzing Stochastic Optimization Algorithms." IEEE Signal Processing Magazine 37, no. 5 (2020): 32–42. http://dx.doi.org/10.1109/msp.2020.3003539.

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Zhu, Jianyun, and Li Chen. "Stochastic Optimization of Onboard Photovoltaic Hybrid Power System Considering Environmental Uncertainties." Journal of Marine Science and Engineering 12, no. 8 (2024): 1240. http://dx.doi.org/10.3390/jmse12081240.

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Environmental uncertainties present a significant challenge in the design of onboard photovoltaic hybrid power systems (PV-HPS), a pivotal decarbonization technology garnering widespread attention in the shipping industry. Neglecting environmental uncertainties associated with photovoltaic (PV) output and hull resistance can lead to suboptimal solutions. To address this issue, this paper proposes a stochastic optimization method for PV-HPS, aiming to minimize greenhouse gas (GHG) emissions and lifecycle costs. Copula functions are employed to establish joint distributions of uncertainties in s

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Tong, Qianping, Xingji Jin, Timo Pukkala, Lihu Dong, and Fengri Li. "Stochastic Optimization of the Management Schedule of Korean Pine Plantations." Forests 15, no. 6 (2024): 935. http://dx.doi.org/10.3390/f15060935.

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Korean pine is one of the most important tree species in northeastern China, where Korean pine plantations produce timber and edible seeds. Often, seeds create more income than timber. Predicting the timber and cone yields of alternative management schedules of the plantations involves uncertainty because the future climatic conditions for tree growth and cone production are unknown. This study developed a simulation model that generates stochastic variation around the predictions of tree growth and cone yield models, allowing the forest manager to seek cutting schedules that maximize the expe

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Mercer, P., S. Sheldon, T. Kotsos, and W. O’Neill. "Detection of Juvenile Sleep Deprivation by Stochastic Optimization of Pupillographic Records." Methods of Information in Medicine 42, no. 03 (2003): 282–86. http://dx.doi.org/10.1055/s-0038-1634362.

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Summary Objective: To address the challenging problem of measuring juvenile sleep deprivation, we test the hypothesis that a pupillographic method found successful for adult narcoleptics might also discriminate between sleep deprived juveniles acting as their own controls. Methods: A linear, nonstationary model relating pupillary diameter and a random photic stimulus are estimated by recursive regressions from pupillographic records of 8 juveniles of median age 7 years acting as their own rested controls. The estimated pupillary impulse response noise functions are stochastically optimized usi

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Chen, Zhi, Melvyn Sim, and Peng Xiong. "Robust Stochastic Optimization Made Easy with RSOME." Management Science 66, no. 8 (2020): 3329–39. http://dx.doi.org/10.1287/mnsc.2020.3603.

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We present a new distributionally robust optimization model called robust stochastic optimization (RSO), which unifies both scenario-tree-based stochastic linear optimization and distributionally robust optimization in a practicable framework that can be solved using the state-of-the-art commercial optimization solvers. We also develop a new algebraic modeling package, Robust Stochastic Optimization Made Easy (RSOME), to facilitate the implementation of RSO models. The model of uncertainty incorporates both discrete and continuous random variables, typically assumed in scenario-tree-based stoc

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Krarup, Jackob. "Stochastics in combinatorial optimization." European Journal of Operational Research 38, no. 1 (1989): 123–24. http://dx.doi.org/10.1016/0377-2217(89)90480-3.

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Yu, Jie, Qizhi Feng, Yang Li, and Jinde Cao. "Stochastic Optimal Dispatch of Virtual Power Plant considering Correlation of Distributed Generations." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/135673.

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Virtual power plant (VPP) is an aggregation of multiple distributed generations, energy storage, and controllable loads. Affected by natural conditions, the uncontrollable distributed generations within VPP, such as wind and photovoltaic generations, are extremely random and relative. Considering the randomness and its correlation of uncontrollable distributed generations, this paper constructs the chance constraints stochastic optimal dispatch of VPP including stochastic variables and its random correlation. The probability distributions of independent wind and photovoltaic generations are de

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Deng, Xi, Gonglin Yuan, and Yuehan Yang. "A Stochastic Inertial Limited Memory BFGS Algorithm Based on the Mathematical Model of Rural Pipeline Network and its Application in Machine Learning." Journal of Physics: Conference Series 2890, no. 1 (2024): 012001. http://dx.doi.org/10.1088/1742-6596/2890/1/012001.

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Abstract Stochastic algorithms are critical in addressing complex rural pipe networks and non-convex stochastic optimization problems. With the development of artificial intelligence, large-scale optimization problems that cannot be solved effectively by traditional optimization methods have emerged. Therefore, stochastic optimization algorithms are rapidly developing in the field of optimization. This paper introduces an inertial extrapolation stochastic BFGS (IESBFGS) algorithm, an innovative amalgamation of the inertial extrapolation technique and the finite memory quasi-Newton algorithm to

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Sihotang, Hengki Tamando, Patricius Michaud Felix, Aisyah Alesha, and Joan De Mathew. "Integration of stochastic and robust optimization techniques into DEA model for more accurate and reliable efficiency estimation." Jurnal Teknik Informatika C.I.T Medicom 14, no. 2 (2022): 1–4. http://dx.doi.org/10.35335/cit.vol14.2022.229.pp1-4.

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Efficiency assessment is vital to assessing decision-making units (DMUs) in numerous sectors. DEA is a prominent non-parametric efficiency assessment tool. Traditional DEA models assume deterministic inputs and outputs, ignoring real-world uncertainties and variability. To improve efficiency estimation, stochastic and robust optimization approaches can be integrated into DEA models. To improve efficiency estimation, we present a stochastic and robust optimization framework incorporating DEA. Probabilistic inputs and outputs allow stochastic optimization to account for uncertainty. The model ca

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Diaz, Rafael, and Barry Charles Ezell. "A Simulation-Based Optimization Approach to a Lost Sale Stochastic Inventory Model." International Journal of Operations Research and Information Systems 3, no. 2 (2012): 46–63. http://dx.doi.org/10.4018/joris.2012040103.

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This paper describes a stochastic inventory model where the control review system is periodic; demand contains auto-correlated components; and categorized as a lost sale case. The authors propose a simulation-based optimization based on using a combination of simulated annealing, pattern search, and ranking and selection methods to search and approximate solutions to this problem. Simulated annealing is employed to stochastically nominate and pre-select solutions in a decision space. Pattern search is used to systematically define a grid of competitive neighbors around pre-selected solutions.

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Sahoo, Laxminarayan, Asoke Kumar Bhunia, and Dilip Roy. "Reliability optimization in stochastic domain via genetic algorithm." International Journal of Quality & Reliability Management 31, no. 6 (2014): 698–717. http://dx.doi.org/10.1108/ijqrm-06-2011-0090.

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Purpose – The purpose of this paper is to formulate the reliability optimization problem in stochastic and interval domain and also to solve the same under different stochastic set up. Design/methodology/approach – Stochastic programming technique has been used to convert the chance constraints into deterministic form and the corresponding problem is transformed to mixed-integer constrained optimization problem with interval objective. Then the reduced problem has been converted to unconstrained optimization problem with interval objective by Big-M penalty technique. The resulting problem has

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Fouskakis, Dimitris, and David Draper. "Stochastic Optimization: A Review." International Statistical Review / Revue Internationale de Statistique 70, no. 3 (2002): 315. http://dx.doi.org/10.2307/1403861.

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