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Journal articles on the topic 'Stochastic simulation algorithms'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Mooasvi, Azam, and Adrian Sandu. "APPROXIMATE EXPONENTIAL ALGORITHMS TO SOLVE THE CHEMICAL MASTER EQUATION." Mathematical Modelling and Analysis 20, no. 3 (2015): 382–95. http://dx.doi.org/10.3846/13926292.2015.1048760.

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This paper discusses new simulation algorithms for stochastic chemical kinetics that exploit the linearity of the chemical master equation and its matrix exponential exact solution. These algorithms make use of various approximations of the matrix exponential to evolve probability densities in time. A sampling of the approximate solutions of the chemical master equation is used to derive accelerated stochastic simulation algorithms. Numerical experiments compare the new methods with the established stochastic simulation algorithm and the tau-leaping method.

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Stutz, Timothy C., Alfonso Landeros, Jason Xu, Janet S. Sinsheimer, Mary Sehl, and Kenneth Lange. "Stochastic simulation algorithms for Interacting Particle Systems." PLOS ONE 16, no. 3 (2021): e0247046. http://dx.doi.org/10.1371/journal.pone.0247046.

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Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source program

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Konopel'kin, M. Yu, S. V. Petrov, and D. A. Smirnyagina. "Implementation of stochastic signal processing algorithms in radar CAD." Russian Technological Journal 10, no. 5 (2022): 49–59. http://dx.doi.org/10.32362/2500-316x-2022-10-5-49-59.

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Objectives. In 2020, development work on the creation of a Russian computer-assisted design system for radars (radar CAD) was completed. Radar CAD provides extensive opportunities for creating simulation models for developing the hardware-software complex of radar algorithms, which take into account the specific conditions of aerospace environment observation. The purpose of the present work is to review and demonstrate the capabilities of radar CAD in terms of implementing and testing algorithms for processing stochastic signals.Methods. The work is based on the mathematical apparatus of line

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Wieder, Nicolas, Rainer H. A. Fink, and Frederic von Wegner. "Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics." Journal of Biomedicine and Biotechnology 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/572492.

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In simulations of chemical systems, the main task is to find an exact or approximate solution of thechemical master equation(CME) that satisfies certain constraints with respect to computation time and accuracy. WhileBrownian motionsimulations of single molecules are often too time consuming to represent the mesoscopic level, the classicalGillespie algorithmis a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains.Gillespie's algorithmcan be approximated via thetau-leapmethod and thechemical Langevin equation(CLE). Both methods lead to a

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Ding, Liangliang, Jingyuan Zhou, Wenhui Tang, Xianwen Ran, and Ye Cheng. "Research on the Crushing Process of PELE Casing Material Based on the Crack-Softening Algorithm and Stochastic Failure Algorithm." Materials 11, no. 9 (2018): 1561. http://dx.doi.org/10.3390/ma11091561.

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In order to more realistically reflect the penetrating and crushing process of a PELE (Penetration with Enhanced Lateral Efficiency) projectile, the stochastic failure algorithm and crack-softening algorithm were added to the corresponding material in this paper. According to the theoretical analysis of the two algorithms, the material failure parameters (stochastic constant γ, fracture energy Gf, and tensile strength σT) were determined. Then, four sets of simulation conditions ((a) no crack softening, (b) no stochastic failure, (c) no crack softening and no stochastic failure, and (d) crack

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Altıntan, Derya, Vi̇lda Purutçuoğlu, and Ömür Uğur. "Impulsive Expressions in Stochastic Simulation Algorithms." International Journal of Computational Methods 15, no. 01 (2017): 1750075. http://dx.doi.org/10.1142/s021987621750075x.

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Jumps can be seen in many natural processes. Classical deterministic modeling approach explains the dynamical behavior of such systems by using impulsive differential equations. This modeling strategy assumes that the dynamical behavior of the whole system is deterministic, continuous, and it adds jumps to the state vector at certain times. Although deterministic approach is satisfactory in many cases, it is a well-known fact that stochasticity or uncertainty has crucial importance for dynamical behavior of many others. In this study, we propose to include this abrupt change in the stochastic

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Zhang, Ce, Xiangxiang Meng, and Yan Ji. "Parameter Estimation of Fractional Wiener Systems with the Application of Photovoltaic Cell Models." Mathematics 11, no. 13 (2023): 2945. http://dx.doi.org/10.3390/math11132945.

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Fractional differential equations are used to construct mathematical models and can describe the characteristics of real systems. In this paper, the parameter estimation problem of a fractional Wiener system is studied by designing linear filters which can obtain smaller tunable parameters and maintain the stability of the parameters in any case. To improve the identification performance of the stochastic gradient algorithm, this paper derives two modified stochastic gradient algorithms for the fractional nonlinear Wiener systems with colored noise. By introducing the forgetting factor, a forg

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Tan, Min Keng, Helen Sin Ee Chuo, Kit Guan Lim, Renee Ka Yin Chin, Soo Siang Yang, and Kenneth Tze Kin Teo. "A COMPARISON STUDY OF DETERMINISTIC AND METAHEURISTIC ALGORITHMS FOR STOCHASTIC TRAFFIC FLOW OPTIMIZATION UNDER SATURATED CONDITION." ICTACT Journal on Soft Computing 10, no. 3 (2020): 2117–23. https://doi.org/10.21917/ijsc.2020.0301.

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Traffic congestion is a perennial issue for most cities. Various artificial intelligence (AI) algorithms, which can categorize as deterministic and metaheuristic algorithms have been suggested to mitigate congestion. Although traffic flow is dynamic and stochastic in nature, most of the previous works evaluated the algorithms with a deterministic or nonstochastic traffic flow pattern. As such, the adaptiveness of those AI algorithms in dealing with stochastic traffic flow patterns is yet to be investigated. Therefore, this paper aims to explore the feasibility of both algorithm types in contro

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XU, ZI, YINGYING LI, and XINGFANG ZHAO. "SIMULATION-BASED OPTIMIZATION BY NEW STOCHASTIC APPROXIMATION ALGORITHM." Asia-Pacific Journal of Operational Research 31, no. 04 (2014): 1450026. http://dx.doi.org/10.1142/s0217595914500262.

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This paper proposes one new stochastic approximation algorithm for solving simulation-based optimization problems. It employs a weighted combination of two independent current noisy gradient measurements as the iterative direction. It can be regarded as a stochastic approximation algorithm with a special matrix step size. The almost sure convergence and the asymptotic rate of convergence of the new algorithm are established. Our numerical experiments show that it outperforms the classical Robbins–Monro (RM) algorithm and several other existing algorithms for one noisy nonlinear function minimi

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Bhatnagar, Shalabh, Vivek Kumar Mishra, and Nandyala Hemachandra. "Stochastic Algorithms for Discrete Parameter Simulation Optimization." IEEE Transactions on Automation Science and Engineering 8, no. 4 (2011): 780–93. http://dx.doi.org/10.1109/tase.2011.2159375.

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Pérez-García, Aníbal, Nelson Obregón-Neira, and Oscar García-Cabrejo. "Alternative geostatistics tools applied to Morroa aquifer modeling (Sucre-Colombia)." Revista Facultad de Ingeniería Universidad de Antioquia, no. 50 (March 20, 2013): 63–76. http://dx.doi.org/10.17533/udea.redin.14932.

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In this paper, stochastic simulations and some multiple-point geostatistical principles are developed using a comparative methodology consisting of two phases. First, a traditional methodology based on variogram concepts called Conditional Stochastic Simulation (CSS) and more specifically the Sequential Indicator Simulation (SISIM) is applied to the aquifer modeling. Secondly, modern algorithm based on multiple-point geostatistics called SNESIM [1] and ζ Model [4] are implemented to integrate information available in the definition of Morroa aquifer facies. The simulations are realized by usin

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Wang, Dongqing, Tong Shan, and Rui Ding. "DATA FILTERING BASED STOCHASTIC GRADIENT ALGORITHMS FOR MULTIVARIABLE CARAR-LIKE SYSTEMS." Mathematical Modelling and Analysis 18, no. 3 (2013): 374–85. http://dx.doi.org/10.3846/13926292.2013.804889.

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This paper considers identification problems for a multivariable controlled autoregressive system with autoregressive noises. A hierarchical generalized stochastic gradient algorithm and a filtering based hierarchical stochastic gradient algorithm are presented to estimate the parameter vectors and parameter matrix of such multivariable colored noise systems, by using the hierarchical identification principle. The simulation results show that the proposed hierarchical gradient estimation algorithms are effective.

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Ding, Xiaodong, and Chengliang Wang. "A Novel Algorithm of Stochastic Chance-Constrained Linear Programming and Its Application." Mathematical Problems in Engineering 2012 (2012): 1–17. http://dx.doi.org/10.1155/2012/139271.

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The computation problem is discussed for the stochastic chance-constrained linear programming, and a novel direct algorithm, that is, simplex algorithm based on stochastic simulation, is proposed. The considered programming problem in this paper is linear programming with chance constraints and random coefficients, and therefore the stochastic simulation is an important implement of the proposed algorithm. By theoretical analysis, the theory basis of the proposed algorithm is obtained and, by numerical examples, the feasibility and validness of this algorithm are illustrated. The detailed algo

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Braun, Daniel, and Ronny Müller. "Stochastic emulation of quantum algorithms." New Journal of Physics 24, no. 2 (2022): 023028. http://dx.doi.org/10.1088/1367-2630/ac4b0f.

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Abstract Quantum algorithms profit from the interference of quantum states in an exponentially large Hilbert space and the fact that unitary transformations on that Hilbert space can be broken down to universal gates that act only on one or two qubits at the same time. The former aspect renders the direct classical simulation of quantum algorithms difficult. Here we introduce higher-order partial derivatives of a probability distribution of particle positions as a new object that shares these basic properties of quantum mechanical states needed for a quantum algorithm. Discretization of the po

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Tian, Heng, Fuhai Duan, Yong Sang, and Liang Fan. "Novel algorithms for sequential fault diagnosis based on greedy method." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 234, no. 6 (2020): 779–92. http://dx.doi.org/10.1177/1748006x20914498.

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Test sequencing for binary systems is a nondeterministic polynomial-complete problem, where greedy algorithms have been proposed to find the solution. The traditional greedy algorithms only extract a single kind of information from the D-matrix to search the optimal test sequence, so their application scope is limited. In this study, two novel greedy algorithms that combine the weight index for fault detection with the information entropy are introduced for this problem, which are defined as the Mix1 algorithm and the Mix2 algorithm. First, the application scope for the traditional greedy algo

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Hedar, Abdel-Rahman, Amira Allam, and Alaa Abdel-Hakim. "Simulation-Based EDAs for Stochastic Programming Problems." Computation 8, no. 1 (2020): 18. http://dx.doi.org/10.3390/computation8010018.

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With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating

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Liu, Ding-Peng, Tsung-Yueh Lin, and Hsin-Haou Huang. "Improving the Computational Efficiency for Optimization of Offshore Wind Turbine Jacket Substructure by Hybrid Algorithms." Journal of Marine Science and Engineering 8, no. 8 (2020): 548. http://dx.doi.org/10.3390/jmse8080548.

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When solving real-world problems with complex simulations, utilizing stochastic algorithms integrated with a simulation model appears inefficient. In this study, we compare several hybrid algorithms for optimizing an offshore jacket substructure (JSS). Moreover, we propose a novel hybrid algorithm called the divisional model genetic algorithm (DMGA) to improve efficiency. By adding different methods, namely particle swarm optimization (PSO), pattern search (PS) and targeted mutation (TM) in three subpopulations to become “divisions,” each division has unique functionalities. With the collabora

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D’Agostino, Daniele, Giulia Pasquale, Andrea Clematis, et al. "Parallel Solutions for Voxel-Based Simulations of Reaction-Diffusion Systems." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/980501.

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There is an increasing awareness of the pivotal role of noise in biochemical processes and of the effect of molecular crowding on the dynamics of biochemical systems. This necessity has given rise to a strong need for suitable and sophisticated algorithms for the simulation of biological phenomena taking into account both spatial effects and noise. However, the high computational effort characterizing simulation approaches, coupled with the necessity to simulate the models several times to achieve statistically relevant information on the model behaviours, makes such kind of algorithms very ti

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Xu, Haiqing. "Stochastic simulation methods in the study of cell rhythm." Applied and Computational Engineering 32, no. 1 (2024): 106–10. http://dx.doi.org/10.54254/2755-2721/32/20230191.

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Stochastic simulation methods play a crucial role in the study of cellular rhythms. Based on the characteristics of stochastic algorithms, we can more accurately capture the noise effects existing in biological systems and explore their impact on cell rhythms. The findings from stochastic simulation methods shed light on how cell rhythms operate at the molecular level, and this paper presents them inductively for different algorithm types, enabling a deeper understanding of their characteristics. Furthermore, based on the analysis of existing studies, this paper finds that a stochastic simulat

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20

Kuo, Cheng Chien, Hung Cheng Chen, Teng Fa Taso, and Chin Ming Chiang. "A Modified Particle Swarm Optimization Algorithm with Cases Studies." Advanced Materials Research 268-270 (July 2011): 823–28. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.823.

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s paper presents a hybrid algorithm, the “particle swarm optimization with simulated annealing behavior (SA-PSO)” algorithm, which combines the advantages of good solution quality in simulated annealing and fast calculation in particle swarm optimization. As stochastic optimization algorithms are sensitive to its parameters, this paper introduces criteria in selecting parameters to improve solution quality. To prove the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimized functions of different dimensions. The results made f

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Bhatnagar, Shalabh, N. Hemachandra, and Vivek Kumar Mishra. "Stochastic approximation algorithms for constrained optimization via simulation." ACM Transactions on Modeling and Computer Simulation 21, no. 3 (2011): 1–22. http://dx.doi.org/10.1145/1921598.1921599.

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Jeschke, Matthias, Roland Ewald, and Adelinde M. Uhrmacher. "Exploring the performance of spatial stochastic simulation algorithms." Journal of Computational Physics 230, no. 7 (2011): 2562–74. http://dx.doi.org/10.1016/j.jcp.2010.12.030.

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Xu, Wenlong. "Conditional curvilinear stochastic simulation using pixel-based algorithms." Mathematical Geology 28, no. 7 (1996): 937–49. http://dx.doi.org/10.1007/bf02066010.

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Ozguven, Eren Erman, and Kaan Ozbay. "Simultaneous Perturbation Stochastic Approximation Algorithm for Solving Stochastic Problems of Transportation Network Analysis." Transportation Research Record: Journal of the Transportation Research Board 2085, no. 1 (2008): 12–20. http://dx.doi.org/10.3141/2085-02.

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Stochastic optimization has become one of the important modeling approaches in transportation network analysis. For example, for traffic assignment problems based on stochastic simulation, it is necessary to use a mathematical algorithm that iteratively seeks out the optimal, the suboptimal solution, or both, because an analytical (closed-form) objective function is not available. Therefore, efficient stochastic approximation algorithms that can find optimal or suboptimal solutions to these problems are needed. The method of successive averages (MSA), a well-known algorithm, is used to solve b

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Luo, Jun Jie, Cheng Su, and Da Jian Han. "A Spectral Representation Model for Simulation of Multivariate Random Processes." Advanced Materials Research 368-373 (October 2011): 1253–58. http://dx.doi.org/10.4028/www.scientific.net/amr.368-373.1253.

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A model is proposed to simulate multivariate weakly stationary Gaussian stochastic processes based on the spectral representation theorem. In this model, the amplitude, phase angle, and frequency involved in the harmonic function are random so that the generated samples are real stochastic processes. Three algorithms are then adopted to improve the simulation efficiency. A uniform cubic B-spline interpolation method is employed to fit the target factorized power spectral density function curves. A recursive algorithm for the Cholesky factorization is utilized to decompose the cross-power spect

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Scholz, Klaus. "Stochastic simulation of urbanhydrological processes." Water Science and Technology 36, no. 8-9 (1997): 25–31. http://dx.doi.org/10.2166/wst.1997.0639.

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Calculations in urban hydrology have almost exclusively been of deterministic character and give therefore unequivocal results. Uncertainties, which are always present, can not been eliminated by more complex models. To take uncertainties into account stochastic algorithms are integrated into hydrological components. A stochastic-hydrological method has developed which can be used to various problems. In contrast to the usual purely deterministic models the model makes it possible to get concrete information of liability of the calibration and prognosis regarding confidence limits The model is

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Koerkamp, Bas Groot, Theo Stijnen, Milton C. Weinstein, and M. G. Myriam Hunink. "The Combined Analysis of Uncertainty and Patient Heterogeneity in Medical Decision Models." Medical Decision Making 31, no. 4 (2010): 650–61. http://dx.doi.org/10.1177/0272989x10381282.

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The analysis of both patient heterogeneity and parameter uncertainty in decision models is increasingly recommended. In addition, the complexity of current medical decision models commonly requires simulating individual subjects, which introduces stochastic uncertainty. The combined analysis of uncertainty and heterogeneity often involves complex nested Monte Carlo simulations to obtain the model outcomes of interest. In this article, the authors distinguish eight model types, each dealing with a different combination of patient heterogeneity, parameter uncertainty, and stochastic uncertainty.

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Kabanov, A. A., and S. A. Dubovik. "Simulation of Rare Events in Stochastic Systems." Journal of Physics: Conference Series 2096, no. 1 (2021): 012151. http://dx.doi.org/10.1088/1742-6596/2096/1/012151.

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Abstract The paper presents algorithms for simulation rare events in stochastic systems based on the theory of large deviations. Here, this approach is used in conjunction with the tools of optimal control theory to estimate the probability that some observed states in a stochastic system will exceed a given threshold by some upcoming time instant. Algorithms for obtaining controlled extremal trajectory (A-profile) of the system, along which the transition to a rare event (threshold) occurs most likely under the influence of disturbances that minimize the action functional, are presented. It i

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Li, Zhihao, Qingtao Wu, Moli Zhang, Lin Wang, Youming Ge, and Guoyong Wang. "Stochastic Zeroth-Order Multi-Gradient Algorithm for Multi-Objective Optimization." Mathematics 13, no. 4 (2025): 627. https://doi.org/10.3390/math13040627.

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Multi-objective optimization (MOO) has become an important method in machine learning, which involves solving multiple competing objective problems simultaneously. Nowadays, many MOO algorithms assume that gradient information is easily available and use this information to optimize functions. However, when encountering situations where gradients are not available, such as black-box functions or non-differentiable functions, these algorithms become ineffective. In this paper, we propose a zeroth-order MOO algorithm named SZMG (stochastic zeroth-order multi-gradient algorithm), which approximat

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Yiou, Pascal, and Aglaé Jézéquel. "Simulation of extreme heat waves with empirical importance sampling." Geoscientific Model Development 13, no. 2 (2020): 763–81. http://dx.doi.org/10.5194/gmd-13-763-2020.

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Abstract. Simulating ensembles of extreme events is a necessary task to evaluate their probability distribution and analyze their meteorological properties. Algorithms of importance sampling have provided a way to simulate trajectories of dynamical systems (like climate models) that yield extreme behavior, like heat waves. Such algorithms also give access to the return periods of such events. We present an adaptation based on circulation analogues of importance sampling to provide a data-based algorithm that simulates extreme events like heat waves in a realistic way. This algorithm is a modif

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Ben Halima Abid, Donia, Saif Eddine Abouda, Hanane Medhaffar, and Mohamed Chtourou. "An Improved Method for Stochastic Nonlinear System’s Identification Using Fuzzy-Type Output-Error Autoregressive Hammerstein–Wiener Model Based on Gradient Algorithm, Multi-Innovation, and Data Filtering Techniques." Complexity 2021 (August 20, 2021): 1–29. http://dx.doi.org/10.1155/2021/8525090.

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This paper proposes an innovative identification approach of nonlinear stochastic systems using Hammerstein–Wiener (HW) model with output-error autoregressive (OEA) noise. Two fuzzy systems are suggested for the identification of the input and output nonlinear blocks of a proposed model from given input-output data measurements. In this work, the need for the commonly used assumptions including well-known structure of input and/or output nonlinearities and/or reversible nonlinear output is eliminated by replacing the intermediate variables and noise with their estimates. Four parametric estima

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Yu, Hang, Yu Zhang, Pengxing Cai, Junyan Yi, Sheng Li, and Shi Wang. "Stochastic Multiple Chaotic Local Search-Incorporated Gradient-Based Optimizer." Discrete Dynamics in Nature and Society 2021 (December 2, 2021): 1–16. http://dx.doi.org/10.1155/2021/3353926.

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In this study, a hybrid metaheuristic algorithm chaotic gradient-based optimizer (CGBO) is proposed. The gradient-based optimizer (GBO) is a novel metaheuristic inspired by Newton’s method which has two search strategies to ensure excellent performance. One is the gradient search rule (GSR), and the other is local escaping operation (LEO). GSR utilizes the gradient method to enhance ability of exploitation and convergence rate, and LEO employs random operators to escape the local optima. It is verified that gradient-based metaheuristic algorithms have obvious shortcomings in exploration. Meanw

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Karimi, Mohammad, Maryam Miriestahbanati, Hamed Esmaeeli, and Ciprian Alecsandru. "Multi-Objective Stochastic Optimization Algorithms to Calibrate Microsimulation Models." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (2019): 743–52. http://dx.doi.org/10.1177/0361198119838260.

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The calibration process for microscopic models can be automatically undertaken using optimization algorithms. Because of the random nature of this problem, the corresponding objectives are not simple concave functions. Accordingly, such problems cannot easily be solved unless a stochastic optimization algorithm is used. In this study, two different objectives are proposed such that the simulation model reproduces real-world traffic more accurately, both in relation to longitudinal and lateral movements. When several objectives are defined for an optimization problem, one solution method may ag

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Chen, Bo, Yilin Zhou, Zhaoyi Li, Jingjing Jia, and Yirui Zhang. "Adaptive Optical Closed-Loop Control Based on the Single-Dimensional Perturbation Descent Algorithm." Sensors 23, no. 9 (2023): 4371. http://dx.doi.org/10.3390/s23094371.

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Modal-free optimization algorithms do not require specific mathematical models, and they, along with their other benefits, have great application potential in adaptive optics. In this study, two different algorithms, the single-dimensional perturbation descent algorithm (SDPD) and the second-order stochastic parallel gradient descent algorithm (2SPGD), are proposed for wavefront sensorless adaptive optics, and a theoretical analysis of the algorithms’ convergence rates is presented. The results demonstrate that the single-dimensional perturbation descent algorithm outperforms the stochastic pa

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Jin, Ziyan, Xinyi Zhou, and Zhaoyuan Fang. "DelaySSA: stochastic simulation of biochemical systems and gene regulatory networks with or without time delays." PLOS Computational Biology 21, no. 4 (2025): e1012919. https://doi.org/10.1371/journal.pcbi.1012919.

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Stochastic Simulation Algorithm (SSA) is crucial for modeling biochemical reactions and gene regulatory networks. Traditional SSA is characterized by Markovian property and cannot naturally model systems with time delays. Several algorithms have already been designed to handle delayed reactions, yet few easy-to-use implementations exist. To address these challenges, we have developed DelaySSA, an R package that implements currently available algorithms for SSA with or without delays. Meanwhile, we also provided Matlab and Python versions to support wider applications. We demonstrated its accur

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Merheb, Abdel-Razzak, Hassan Noura, and François Bateman. "Mathematical Modeling of Ecological Systems Algorithm." Lebanese Science Journal 22, no. 2 (2022): 209–31. http://dx.doi.org/10.22453/lsj-022.2.209-231.

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In this paper, the mathematical modeling of a new bio-inspired evolutionary search algorithm called Ecological Systems Algorithm (ESA) is presented. ESA imitates ecological rules to find iteratively the optimum of a given function through interaction between predator and prey search species. ESA is then compared to the well-known Genetic Algorithm which is a powerful bio-inspired stochastic search/optimization algorithm used for decades. Simulation results of the two algorithms optimizing ten different benchmark functions are used to investigate and compare both algorithms based on their speed

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Mongwe, Wilson Tsakane, Rendani Mbuvha, and Tshilidzi Marwala. "Locally Scaled and Stochastic Volatility Metropolis– Hastings Algorithms." Algorithms 14, no. 12 (2021): 351. http://dx.doi.org/10.3390/a14120351.

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Markov chain Monte Carlo (MCMC) techniques are usually used to infer model parameters when closed-form inference is not feasible, with one of the simplest MCMC methods being the random walk Metropolis–Hastings (MH) algorithm. The MH algorithm suffers from random walk behaviour, which results in inefficient exploration of the target posterior distribution. This method has been improved upon, with algorithms such as Metropolis Adjusted Langevin Monte Carlo (MALA) and Hamiltonian Monte Carlo being examples of popular modifications to MH. In this work, we revisit the MH algorithm to reduce the aut

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Mohamed, Linah, Mike Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." SPE Journal 15, no. 01 (2009): 31–38. http://dx.doi.org/10.2118/119139-pa.

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Summary History matching and uncertainty quantification are two important research topics in reservoir simulation currently. In the Bayesian approach, we start with prior information about a reservoir (e.g., from analog outcrop data) and update our reservoir models with observations (e.g., from production data or time-lapse seismic). The goal of this activity is often to generate multiple models that match the history and use the models to quantify uncertainties in predictions of reservoir performance. A critical aspect of generating multiple history-matched models is the sampling algorithm us

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Barone, Piero, and Arnolodo Frigessi. "Improving Stochastic Relaxation for Gussian Random Fields." Probability in the Engineering and Informational Sciences 4, no. 3 (1990): 369–89. http://dx.doi.org/10.1017/s0269964800001674.

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In this paper, we are concerned with the simulation of Gaussian random fields by means of iterative stochastic algorithms, which are compared in terms of rate of convergence. A parametrized class of algorithms, which includes stochastic relaxation (Gibbs sampler), is proposed and its convergence properties are established. A suitable choice for the parameter improves the rate of convergence with respect to stochastic relaxation for special classes of covariance matrices. Some examples and numerical experiments are given.

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Kiss, Oriel, Michele Grossi, and Alessandro Roggero. "Importance sampling for stochastic quantum simulations." Quantum 7 (April 13, 2023): 977. http://dx.doi.org/10.22331/q-2023-04-13-977.

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Simulating many-body quantum systems is a promising task for quantum computers. However, the depth of most algorithms, such as product formulas, scales with the number of terms in the Hamiltonian, and can therefore be challenging to implement on near-term, as well as early fault-tolerant quantum devices. An efficient solution is given by the stochastic compilation protocol known as qDrift, which builds random product formulas by sampling from the Hamiltonian according to the coefficients. In this work, we unify the qDrift protocol with importance sampling, allowing us to sample from arbitrary

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Ma, Yimin, and Shuli Sun. "Distributed Optimal and Self-Tuning Filters Based on Compressed Data for Networked Stochastic Uncertain Systems with Deception Attacks." Sensors 23, no. 1 (2022): 335. http://dx.doi.org/10.3390/s23010335.

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In this study, distributed security estimation problems for networked stochastic uncertain systems subject to stochastic deception attacks are investigated. In sensor networks, the measurement data of sensor nodes may be attacked maliciously in the process of data exchange between sensors. When the attack rates and noise variances for the stochastic deception attack signals are known, many measurement data received from neighbour nodes are compressed by a weighted measurement fusion algorithm based on the least-squares method at each sensor node. A distributed optimal filter in the linear mini

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Zhang, Yanhui, and Wenyu Yang. "A comparative study of the stochastic simulation methods applied in structural health monitoring." Engineering Computations 31, no. 7 (2014): 1484–513. http://dx.doi.org/10.1108/ec-07-2013-0185.

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Purpose – The purpose of this paper is to discuss the characteristics of several stochastic simulation methods applied in computation issue of structure health monitoring (SHM). Design/methodology/approach – On the basis of the previous studies, this research focusses on four promising methods: transitional Markov chain Monte Carlo (TMCMC), slice sampling, slice-Metropolis-Hasting (M-H), and TMCMC-slice algorithm. The slice-M-H is the improved slice sampling algorithm, and the TMCMC-slice is the improved TMCMC algorithm. The performances of the parameters samples generated by these four algori

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Zakovryashin, Andrey V., and Sergei M. Prigarin. "Numerical simulation of optical phenomena in atmospheric clouds and fogs." Russian Journal of Numerical Analysis and Mathematical Modelling 35, no. 6 (2020): 367–75. http://dx.doi.org/10.1515/rnam-2020-0030.

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AbstractWe present algorithms for fast computation of phase functions of atmospheric clouds and for stochastic simulation of such optical phenomena as fogbows, glories, coronas and halos. Using the developed numerical algorithms and software, we analyze optical phenomena for several cloud and fog models.

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Mojtahedzadeh Larijani, Mostafa, Mehrdad Ahmadi Kamarposhti, and Tohid Nouri. "Stochastic Unit Commitment Study in a Power System with Flexible Load in Presence of High Penetration Renewable Farms." International Journal of Energy Research 2023 (July 7, 2023): 1–19. http://dx.doi.org/10.1155/2023/9979610.

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In this paper, a new hybrid multiobjective algorithm, namely, the modified bald eagle search Algorithm (MBES), integrated with the grasshopper optimization algorithm, is proposed to solve the unit commitment (UC) problem. We consider a standard 10-unit power system with two wind farms, two photovoltaic farms, and flexible loads for optimization purposes. The UC problem is tackled under uncertainties related to demand and renewable generation capacities. To account for these uncertainties, probability density functions (PDFs) are assigned to the sources of uncertainty, and Monte Carlo simulatio

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CHAI, RUISHUAI. "FRACTAL DIMENSION OF FRACTIONAL BROWNIAN MOTION BASED ON RANDOM SETS." Fractals 28, no. 08 (2020): 2040020. http://dx.doi.org/10.1142/s0218348x20400204.

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The fractal dimension of fractional Brownian motion can effectively describe random sets, reflecting the regularity implicit in complex random sets. Data mining algorithms based on fractal theory usually follow the calculation of the fractal dimension of fractional Brownian motion. However, the existing fractal dimension calculation methods of fractal Brownian motion have high time complexity and space complexity, which greatly reduces the efficiency of the algorithm and makes it difficult for the algorithm to adapt to high-speed and massive data flow environments. Therefore, several existing

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Nikolic, Kostantin. "Training Neural Network Elements Created From Long Shot Term Memory." Oriental journal of computer science and technology 10, no. 1 (2017): 01–10. http://dx.doi.org/10.13005/ojcst/10.01.01.

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This paper presents the application of stochastic search algorithms to train artificial neural networks. Methodology approaches in the work created primarily to provide training complex recurrent neural networks. It is known that training recurrent networks is more complex than the type of training feedforward neural networks. Through simulation of recurrent networks is realized propagation signal from input to output and training process achieves a stochastic search in the space of parameters. The performance of this type of algorithm is superior to most of the training algorithms, which are

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Liu, Pai, Xi Zhang, Zhongshun Shi, and Zewen Huang. "Simulation Optimization for MRO Systems Operations." Asia-Pacific Journal of Operational Research 34, no. 02 (2017): 1750003. http://dx.doi.org/10.1142/s0217595917500038.

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In this paper, we address the scheduling issues in a class of maintenance, repair and overhaul systems. By considering all key characteristics such as disassembly, material recovery uncertainty, material matching requirements, stochastic routings and variable processing times, the scheduling problem is formulated into a simulation optimization problem. To solve this difficult problem, we developed two hybrid algorithms based on nested partitions method and optimal computing budged allocation technology. Asymptotic convergence of these two algorithms is proved and numerical results show that th

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Purnomo, Muhammad Ridwan Andi. "Optimisation-in-the-loop simulation of multi products single vendor-multi buyers supply chain systems with reactive lateral transhipment." Jurnal Sistem dan Manajemen Industri 7, no. 2 (2023): 116–26. http://dx.doi.org/10.30656/jsmi.v7i2.6495.

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Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers.

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Warne, David J., Ruth E. Baker, and Matthew J. Simpson. "Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art." Journal of The Royal Society Interface 16, no. 151 (2019): 20180943. http://dx.doi.org/10.1098/rsif.2018.0943.

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Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour

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Yu, Nan, Bin Dong, Yuben Qu, et al. "Drones Routing with Stochastic Demand." Drones 7, no. 6 (2023): 362. http://dx.doi.org/10.3390/drones7060362.

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Motivated by the increasing number of drones used for package delivery, we first study the problem of Multiple drOne collaborative Routing dEsign (MORE) in this article. That is, given a fixed number of drones and customers, determining the delivery trip for drones under capacity constraint with stochastic demand for customers such that the overall expected traveling cost is minimized. To address the MORE problem, we first prove that MORE falls into the realm of the classical vehicle routing problem with stochastic demand and then propose an effective algorithm for MORE. Next, we have a scheme

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