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Journal articles on the topic 'Solution adaptive'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

LIN, Ying, Liang GE, and Run-liang DOU. "Adaptive error-check solution." Journal of Computer Applications 29, no. 1 (May 31, 2009): 5–8. http://dx.doi.org/10.3724/sp.j.1087.2009.00005.

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2

Zielinnski, K., T. Szydlo, R. Szymacha, J. Kosinski, J. Kosinska, and M. Jarzab. "Adaptive SOA Solution Stack." IEEE Transactions on Services Computing 5, no. 2 (April 2012): 149–63. http://dx.doi.org/10.1109/tsc.2011.8.

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3

Khodier, Ahmed M. M., and Adel Y. Hassan. "One-dimensional adaptive grid generation." International Journal of Mathematics and Mathematical Sciences 20, no. 3 (1997): 577–84. http://dx.doi.org/10.1155/s0161171297000781.

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In this work, we give an adaptive grid generation method which allows a single point to be added in the regions of large variation. This method uses a quadrature rule as a weight function. Our weight function measures the variation of the solution function on each subinterval of the solution domain. The method is applied to obtain the numerical solutions of some differential equations. A comparison of the numerical solution obtained by this method and other methods is given.
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4

Al-Afandi, Jalal, and András Horváth. "Adaptive Gene Level Mutation." Algorithms 14, no. 1 (January 9, 2021): 16. http://dx.doi.org/10.3390/a14010016.

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Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.
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Al-afandi, Jalal, and Horváth András. "Adaptive Gene Level Mutation." Algorithms 14, no. 1 (January 9, 2021): 16. http://dx.doi.org/10.3390/a14010016.

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Genetic Algorithms are stochastic optimization methods where solution candidates, complying to a specific problem representation, are evaluated according to a predefined fitness function. These approaches can provide solutions in various tasks even, where analytic solutions can not be or are too complex to be computed. In this paper we will show, how certain set of problems are partially solvable allowing us to grade segments of a solution individually, which results local and individual tuning of mutation parameters for genes. We will demonstrate the efficiency of our method on the N-Queens and travelling salesman problems where we can demonstrate that our approach always results faster convergence and in most cases a lower error than the traditional approach.
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6

Gu, Yaguang, and Guanghui Hu. "A Third Order Adaptive ADER Scheme for One Dimensional Conservation Laws." Communications in Computational Physics 22, no. 3 (July 6, 2017): 829–51. http://dx.doi.org/10.4208/cicp.oa-2016-0088.

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AbstractWe introduce a third order adaptive mesh method to arbitrary high order Godunov approach. Our adaptive mesh method consists of two parts, i.e., mesh-redistribution algorithm and solution algorithm. The mesh-redistribution algorithm is derived based on variational approach, while a new solution algorithm is developed to preserve high order numerical accuracy well. The feature of proposed Adaptive ADER scheme includes that 1). all simulations in this paper are stable for large CFL number, 2). third order convergence of the numerical solutions is successfully observed with adaptive mesh method, and 3). high resolution and non-oscillatory numerical solutions are obtained successfully when there are shocks in the solution. A variety of numerical examples show the feature well.
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7

Hoppe, Ronald H. W., and Barbara Wohlmuth. "Efficient numerical solution of mixed finite element discretizations by adaptive multilevel methods." Applications of Mathematics 40, no. 3 (1995): 227–48. http://dx.doi.org/10.21136/am.1995.134292.

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8

Wang, Y., L. Fan, D. He, and R. Tafazolli. "Solution to weight-adaptive fair queuing." Electronics Letters 44, no. 5 (2008): 385. http://dx.doi.org/10.1049/el:20083333.

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9

Crawford, Paul M., and Katsuhiko Yokoyama. "An Adaptive Solution in Laboratory Automation." JAPAN TAPPI JOURNAL 62, no. 8 (2008): 994–98. http://dx.doi.org/10.2524/jtappij.62.994.

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10

Shen, Cherng-Yeu, Helen L. Reed, and Thomas A. Foley. "Shepard's Interpolation for Solution-Adaptive Methods." Journal of Computational Physics 106, no. 1 (May 1993): 52–61. http://dx.doi.org/10.1006/jcph.1993.1090.

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11

Tkachev, Alexander, and Dmitry Chernoivan. "Combined Adaptive Meshfree Method for Modeling Potential Physical Fields." Известия высших учебных заведений. Электромеханика 64, no. 1 (2021): 5–12. http://dx.doi.org/10.17213/0136-3360-2021-1-5-12.

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A procedure for numerical solution of the Dirichlet problem for the Laplace equation, which is often re-duced to modeling potential physical fields in homogeneous media, is described. The approximate solution is proposed to be found using a combined meshfree Monte Carlo method and fundamental solutions, which is implemented in two stages. At the first stage, the element of the best approximation in the linear shell of the fundamental solutions of the Laplace equation is determined. At the second stage, the solution is refined using the potential values found by the Monte Carlo method at individual points in the computational domain. Algorithms are given for finding the defining parameters of both methods used to reduce the error. The procedure for evaluating the accuracy of the found approximate solution of the problem is described. An example of calculating the potential distribution in the angular zone under specified boundary conditions using the combined meshfree method is given. The accuracy of the approximate solution is estimated by comparing it with the exact solution. It is shown that the use of the meshfree method leads to a decrease in the error without a significant increase in the computational resources required for its implementation
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12

Joldes, Grand Roman, Peter Teakle, Adam Wittek, and Karol Miller. "Computation of accurate solutions when using element-free Galerkin methods for solving structural problems." Engineering Computations 34, no. 3 (May 2, 2017): 902–20. http://dx.doi.org/10.1108/ec-01-2016-0017.

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Purpose This paper aims to investigate the application of adaptive integration in element-free Galerkin methods for solving problems in structural and solid mechanics to obtain accurate reference solutions. Design/methodology/approach An adaptive quadrature algorithm which allows user control over integration accuracy, previously developed for integrating boundary value problems, is adapted to elasticity problems. The algorithm allows the development of a convergence study procedure that takes into account both integration and discretisation errors. The convergence procedure is demonstrated using an elasticity problem which has an analytical solution and is then applied to accurately solve a soft-tissue extension problem involving large deformations. Findings The developed convergence procedure, based on the presented adaptive integration scheme, allows the computation of accurate reference solutions for challenging problems which do not have an analytical or finite element solution. Originality/value This paper investigates the application of adaptive quadrature to solid mechanics problems in engineering analysis using the element-free Galerkin method to obtain accurate reference solutions. The proposed convergence procedure allows the user to independently examine and control the contribution of integration and discretisation errors to the overall solution error. This allows the computation of reference solutions for very challenging problems which do not have an analytical or even a finite element solution (such as very large deformation problems).
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13

Petrovic, Predrag. "Possible solution of parallel FIR filter structure." Serbian Journal of Electrical Engineering 2, no. 1 (2005): 21–28. http://dx.doi.org/10.2298/sjee0501021p.

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In this paper, a parallel form FIR adaptive filter structure with RLS (Recursive Least Squares) type adaptive algorithm is proposed. The proposed parallel form FIR structure consists of a recursive orthogonal transform stage and sparse FIR sub filters operating in parallel. The adaptive algorithm used to update coefficient vector of the sparse filters is implemented by using modified Hopfield networks. This structure implements the RLS-type adaptive algorithm, without an explicit matrix inversion avoiding numerical instability problems. Simulation results which show the desirable features of proposed structure are given.
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14

Faravelli, Lucia, Clemente Fuggini, and Filippo Ubertini. "Adaptive Solution for Intelligent Cable Vibration Mitigation." Advances in Science and Technology 56 (September 2008): 137–46. http://dx.doi.org/10.4028/www.scientific.net/ast.56.137.

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The mechanics of cables is caught either by numeric or analytic models which are able to predict the nonlinear dynamic response of this structural elements with the desired level of approximation. Although cable dynamics has been widely explored in the literature, efforts are still required in the field of cable vibration mitigation to conceive an economical, feasible and robust control strategy. An adaptive control strategy combining a distributed passive solution with a semiactive actuation is here proposed for the purpose of reducing the spatial cable vibrations. The effectiveness of the proposed control policy is investigated by means of experimental tests and a suitable numeric scheme.
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15

Luong, Phu V., Joe F. Thompson, and Boyd Gatlin. "Solution-adaptive and quality-enhancing grid generation." Journal of Aircraft 30, no. 2 (March 1993): 227–34. http://dx.doi.org/10.2514/3.48270.

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16

Burstedde, Carsten, Martin Burtscher, Omar Ghattas, Georg Stadler, Tiankai Tu, and Lucas C. Wilcox. "ALPS:A framework for parallel adaptive PDE solution." Journal of Physics: Conference Series 180 (July 1, 2009): 012009. http://dx.doi.org/10.1088/1742-6596/180/1/012009.

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17

Crisafulli, S., and R. R. Bitmead. "Adaptive quantization: solution via nonadaptive linear control." IEEE Transactions on Communications 41, no. 5 (May 1993): 741–48. http://dx.doi.org/10.1109/26.225489.

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18

MATSUNO, Kenichi. "Solution-Adaptive Grid Method Using Elliptic Equation." Journal of the Japan Society for Aeronautical and Space Sciences 43, no. 492 (1995): 26–31. http://dx.doi.org/10.2322/jjsass1969.43.26.

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19

Kertész, Attila, József Dániel Dombi, and József Dombi. "Adaptive Scheduling Solution for Grid Meta-Brokering." Acta Cybernetica 19, no. 1 (2009): 105–23. http://dx.doi.org/10.14232/actacyb.19.1.2009.7.

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20

Sun, M., and K. Takayama. "Error localization in solution-adaptive grid methods." Journal of Computational Physics 190, no. 1 (September 2003): 346–50. http://dx.doi.org/10.1016/s0021-9991(03)00278-x.

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21

Jiang, Ying-chun, and Youming Liu. "Adaptive wavelet solution to the Stokes problem." Acta Mathematicae Applicatae Sinica, English Series 24, no. 4 (October 2008): 613–26. http://dx.doi.org/10.1007/s10255-006-6101-7.

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22

Wu, S., X. Shen, and C. J. Geyer. "Adaptive regularization using the entire solution surface." Biometrika 96, no. 3 (August 20, 2009): 513–27. http://dx.doi.org/10.1093/biomet/asp038.

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23

Farhang-Boroujeny, A., and K. Ayatollahi. "A New Solution to Adaptive Inverse Control." IFAC Proceedings Volumes 24, no. 1 (January 1991): 35–40. http://dx.doi.org/10.1016/s1474-6670(17)51293-8.

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24

Marchetti, Yuliya, and Qing Zhou. "Solution path clustering with adaptive concave penalty." Electronic Journal of Statistics 8, no. 1 (2014): 1569–603. http://dx.doi.org/10.1214/14-ejs934.

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25

Kwon, J. H., and H. K. Jeong. "Solution-adaptive grid generation for compressible flow." Computers & Fluids 25, no. 6 (July 1996): 551–60. http://dx.doi.org/10.1016/0045-7930(96)00012-6.

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26

Benyon, David. "Adaptive systems: A solution to usability problems." User Modelling and User-Adapted Interaction 3, no. 1 (1993): 65–87. http://dx.doi.org/10.1007/bf01099425.

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27

Mehrmann, Volker, and Agnieszka Międlar. "Adaptive solution of elliptic PDE-eigenvalue problems." PAMM 9, no. 1 (December 2009): 583–84. http://dx.doi.org/10.1002/pamm.200910263.

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28

ZHANG, ZHENG, GENG LIU, TIANXIANG LIU, QUANREN ZENG, and LIYAN WU. "AN ADAPTIVE MESHLESS COMPUTATIONAL SYSTEM FOR ELASTOPLASTIC CONTACT PROBLEMS." International Journal of Computational Methods 05, no. 03 (September 2008): 433–47. http://dx.doi.org/10.1142/s0219876208001595.

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An adaptive meshless computational system is introduced in this paper to solve two-dimensional elastoplastic contact mechanics. An adaptive element-free Galerkin-finite element coupling computational model based on the gradient of strain energy and a linear programming technique with the initial stiffness method for elastoplastic contact problems are combined. Principle explanation and program realization are carried out. The modularization concept in software engineering is used, and the adaptive change of the influence domain radius and the elastoplastic property of material are also taken into account by the system. A rigid cylinder making contact with an elastic plane is analyzed to validate the system. Some key parameters in adaptive calculation are studied. The adaptive meshless computational system is also applied to elastoplastic contact of rough surfaces. Comparisons of the adaptive refinement solution with the uniform refinement solution are made, and the results show the satisfactory accuracy and efficiency of the solutions from the adaptive refinement model.
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Wang, Hongli, Bin Guo, Jiaqi Liu, Sicong Liu, Yungang Wu, and Zhiwen Yu. "Context-aware Adaptive Surgery." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 3 (September 9, 2021): 1–22. http://dx.doi.org/10.1145/3478073.

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Deep Neural Networks (DNNs) have made massive progress in many fields and deploying DNNs on end devices has become an emerging trend to make intelligence closer to users. However, it is challenging to deploy large-scale and computation-intensive DNNs on resource-constrained end devices due to their small size and lightweight. To this end, model partition, which aims to partition DNNs into multiple parts to realize the collaborative computing of multiple devices, has received extensive research attention. To find the optimal partition, most existing approaches need to run from scratch under given resource constraints. However, they ignore that resources of devices (e.g., storage, battery power), and performance requirements (e.g., inference latency), are often continuously changing, making the optimal partition solution change constantly during processing. Therefore, it is very important to reduce the tuning latency of model partition to realize the real-time adaption under the changing processing context. To address these problems, we propose the Context-aware Adaptive Surgery (CAS) framework to actively perceive the changing processing context, and adaptively find the appropriate partition solution in real-time. Specifically, we construct the partition state graph to comprehensively model different partition solutions of DNNs by import context resources. Then "the neighbor effect" is proposed, which provides the heuristic rule for the search process. When the processing context changes, CAS adopts the runtime search algorithm, Graph-based Adaptive DNN Surgery (GADS), to quickly find the appropriate partition that satisfies resource constraints under the guidance of the neighbor effect. The experimental results show that CAS realizes adaptively rapid tuning of the model partition solutions in 10ms scale even for large DNNs (2.25x to 221.7x search time improvement than the state-of-the-art researches), and the total inference latency still keeps the same level with baselines.
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Chades, Iadine, Josie Carwardine, Tara Martin, Samuel Nicol, Regis Sabbadin, and Olivier Buffet. "MOMDPs: A Solution for Modelling Adaptive Management Problems." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 267–73. http://dx.doi.org/10.1609/aaai.v26i1.8171.

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In conservation biology and natural resource management, adaptive management is an iterative process of improving management by reducing uncertainty via monitoring. Adaptive management is the principal tool for conserving endangered species under global change, yet adaptive management problems suffer from a poor suite of solution methods. The common approach used to solve an adaptive management problem is to assume the system state is known and the system dynamics can be one of a set of pre-defined models. The solution method used is unsatisfactory, employing value iteration on a discretized belief MDP which restricts the study to very small problems. We show how to overcome this limitation by modelling an adaptive management problem as a restricted Mixed Observability MDP called hidden model MDP (hmMDP). We demonstrate how to simplify the value function, the backup operator and the belief update computation. We show that, although a simplified case of POMDPs, hm-MDPs are PSPACE-complete in the finite-horizon case. We illustrate the use of this model to manage a population of the threatened Gouldian finch, a bird species endemic to Northern Australia. Our simple modelling approach is an important step towards efficient algorithms for solving adaptive management problems.
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Soto, Ricardo, Broderick Crawford, Wenceslao Palma, Eric Monfroy, Rodrigo Olivares, Carlos Castro, and Fernando Paredes. "Top-kBased Adaptive Enumeration in Constraint Programming." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/580785.

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Constraint programming effectively solves constraint satisfaction and optimization problems by basically building, pruning, and exploring a search tree of potential solutions. In this context, a main component is the enumeration strategy, which is responsible for selecting the order in which variables and values are selected to build a possible solution. This process is known to be quite important; indeed a correct selection can reach a solution without failed explorations. However, it is well known that selecting the right strategy is quite challenging as their performance is notably hard to predict. During the last years, adaptive enumeration appeared as a proper solution to this problem. Adaptive enumeration allows the solving algorithm being able to autonomously modifying its strategies in solving time depending on performance information. In this way, the most suitable order for variables and values is employed along the search. In this paper, we present a new and more lightweight approach for performing adaptive enumeration. We incorporate a powerful classification technique named Top-kin order to adaptively select strategies along the resolution. We report results on a set of well-known benchmarks where the proposed approach noticeably competes with classical and modern adaptive enumeration methods for constraint satisfaction.
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32

Reitsma, René, and Stanislav Trubin. "Information Space Partitioning Using Adaptive Voronoi Diagrams." Information Visualization 6, no. 2 (January 2007): 123–38. http://dx.doi.org/10.1057/palgrave.ivs.9500152.

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In this paper, we present and evaluate a Voronoi method for partitioning continuous information spaces. We define the formal characteristics of the problem and discuss several well-known partitioning methods and approaches. We submit that although they all partially solve the problem, they all have shortcomings. As an alternative, we offer an approach based on an adaptive version of the multiplicatively weighted Voronoi diagram. The diagram is ‘adaptive’ because it is computed backwards; that is, the generators' weights are treated as dependent rather than independent variables. We successfully test this adaptive solution using both ideal-typical (artificial) and empirical data. Since the resultant visualizations are meant to be used by human subjects, we then discuss the results of a usability experiment, positioning the adaptive solution against a commonly used rectangular solution and the classic nonweighted Voronoi solution. The results indicate that in terms of usability, both the rectangular and the adaptive Voronoi solution outperform the standard Voronoi solution. In addition, although subjects are better able to gage rectangular area relationships, only the adaptive Voronoi solution satisfies all geometric constraints of weight-proportional partitioning.
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33

Chen, Y. Y., R. J. Hsiao, and M. C. Huang. "Fuzzy Solutions for Two-Dimensional Navier-Stokes Equations." Journal of Mechanics 34, no. 1 (November 23, 2015): 1–10. http://dx.doi.org/10.1017/jmech.2015.92.

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AbstractA new methodology via using an adaptive fuzzy algorithm to obtain solutions of “Two-dimensional Navier-Stokes equations” (2-D NSE) is presented in this investigation. The design objective is to find two fuzzy solutions to satisfy precisely the 2-D NSE frequently encountered in practical applications. In this study, a rough fuzzy solution is formulated with adjustable parameters firstly, and then, a set of adaptive laws for optimally tuning the free parameters in the consequent parts of the proposed fuzzy solutions are derived from minimizing an error cost function which is the square summation of approximation errors of boundary conditions, continuum equation and Navier-Stokes equations. In addition, elegant approximated error bounds between the exact solution and the proposed fuzzy solution with respect to the number of fuzzy rules and solution errors have also been proven. Furthermore, the error equations in mesh points can be proven to converge to zero for the 2-D NSE with two sufficient conditions.
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34

Bouzbita, Safae, Abdellatif El Afia, and Rdouan Faizi. "The behaviour of ACS-TSP algorithm when adapting both pheromone parameters using fuzzy logic controller." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5436. http://dx.doi.org/10.11591/ijece.v10i5.pp5436-5444.

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In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the responsible parameters for the decay of the pheromone trails 𝜉 and 𝜌 using fuzzy logic controller (FLC) applied in the travelling salesman problems (TSP). The purpose of the proposed method is to understand the effect of both parameters 𝜉 and 𝜌 on the performance of the ACS at the level of solution quality and convergence speed towards the best solutions through studying the behavior of the ACS algorithm during this adaptation. The adaptive ACS is compared with the standard one. Computational results show that the adaptive ACS with dynamic adaptation of local pheromone parameter 𝜉 is more effective compared to the standard ACS.
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35

Hill, J., E. E. Popova, D. A. Ham, M. D. Piggott, and M. Srokosz. "Adapting to life: ocean biogeochemical modelling and adaptive remeshing." Ocean Science 10, no. 3 (May 9, 2014): 323–43. http://dx.doi.org/10.5194/os-10-323-2014.

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Abstract. An outstanding problem in biogeochemical modelling of the ocean is that many of the key processes occur intermittently at small scales, such as the sub-mesoscale, that are not well represented in global ocean models. This is partly due to their failure to resolve sub-mesoscale phenomena, which play a significant role in vertical nutrient supply. Simply increasing the resolution of the models may be an inefficient computational solution to this problem. An approach based on recent advances in adaptive mesh computational techniques may offer an alternative. Here the first steps in such an approach are described, using the example of a simple vertical column (quasi-1-D) ocean biogeochemical model. We present a novel method of simulating ocean biogeochemical behaviour on a vertically adaptive computational mesh, where the mesh changes in response to the biogeochemical and physical state of the system throughout the simulation. We show that the model reproduces the general physical and biological behaviour at three ocean stations (India, Papa and Bermuda) as compared to a high-resolution fixed mesh simulation and to observations. The use of an adaptive mesh does not increase the computational error, but reduces the number of mesh elements by a factor of 2–3. Unlike previous work the adaptivity metric used is flexible and we show that capturing the physical behaviour of the model is paramount to achieving a reasonable solution. Adding biological quantities to the adaptivity metric further refines the solution. We then show the potential of this method in two case studies where we change the adaptivity metric used to determine the varying mesh sizes in order to capture the dynamics of chlorophyll at Bermuda and sinking detritus at Papa. We therefore demonstrate that adaptive meshes may provide a suitable numerical technique for simulating seasonal or transient biogeochemical behaviour at high vertical resolution whilst minimising the number of elements in the mesh. More work is required to move this to fully 3-D simulations.
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Yang, Zhonghua, Yuanli Cai, and Ge Li. "Improved Gravitational Search Algorithm Based on Adaptive Strategies." Entropy 24, no. 12 (December 14, 2022): 1826. http://dx.doi.org/10.3390/e24121826.

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The gravitational search algorithm is a global optimization algorithm that has the advantages of a swarm intelligence algorithm. Compared with traditional algorithms, the performance in terms of global search and convergence is relatively good, but the solution is not always accurate, and the algorithm has difficulty jumping out of locally optimal solutions. In view of these shortcomings, an improved gravitational search algorithm based on an adaptive strategy is proposed. The algorithm uses the adaptive strategy to improve the updating methods for the distance between particles, gravitational constant, and position in the gravitational search model. This strengthens the information interaction between particles in the group and improves the exploration and exploitation capacity of the algorithm. In this paper, 13 classical single-peak and multi-peak test functions were selected for simulation performance tests, and the CEC2017 benchmark function was used for a comparison test. The test results show that the improved gravitational search algorithm can address the tendency of the original algorithm to fall into local extrema and significantly improve both the solution accuracy and the ability to find the globally optimal solution.
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37

Machado, Marcelo de Oliveira Costa, Eduardo Barrére, and Jairo Souza. "Solving the Adaptive Curriculum Sequencing Problem with Prey-Predator Algorithm." International Journal of Distance Education Technologies 17, no. 4 (October 2019): 71–93. http://dx.doi.org/10.4018/ijdet.2019100105.

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Adaptive curriculum sequencing (ACS) is still a challenge in the adaptive learning field. ACS is a NP-hard problem especially considering the several constraints of the student and the learning material when selecting a sequence from repositories where several sequences could be chosen. Therefore, this has stimulated several researchers to use evolutionary approaches in the search for satisfactory solutions. This work explores the use of an adaptation of the prey-predator algorithm for the ACS problem. Pedagogical experiments with a real student dataset and convergence experiments with a synthetic dataset have shown that the proposed solution is suitable for the problem, although it is a solution not yet explored in the adaptive learning literature.
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Phongthanapanich, Sutthisak, and Pramote Dechaumphai. "EVALUATION OF COMBINED DELAUNAY TRIANGULATION AND REMESHING FOR FINITE ELEMENT ANALYSIS OF CONDUCTIVE HEAT TRANSFER." Transactions of the Canadian Society for Mechanical Engineering 27, no. 4 (December 2004): 319–39. http://dx.doi.org/10.1139/tcsme-2003-0018.

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A finite element method is combined with the Delaunay triangulation and an adaptive remeshing technique to solve for solutions of both steady-state and transient heat conduction problems. The Delaunay triangulation and the adaptive remeshing technique are explained in detail. The solution accuracy and the effectiveness of the combined procedure are evaluated by heat transfer problems that have exact solutions. These problems include steady-state heat conduction in a square plate subjected to a highly localized surface heating, and a transient heat conduction in a long plate subjected to a moving heat source. The examples demonstrate that the adaptive remeshing technique with the Delaunay triangulation significantly reduce the number of the finite elements required for the problems and, at the same time, increase the analysis solution accuracy as compared to the results produced using uniform finite element meshes.
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39

Zheng, Linjin, M. T. Kotschenreuther, F. L. Waelbroeck, and Y. Todo. "ATEQ: Adaptive toroidal equilibrium code." Physics of Plasmas 29, no. 7 (July 2022): 072503. http://dx.doi.org/10.1063/5.0091015.

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A radially adaptive numerical scheme is developed to solve the Grad–Shafranov equation for axisymmetric magnetohydrodynamic equilibrium. A decomposition with independent solutions is employed in the radial direction, and Fourier decomposition is used in the poloidal direction. The independent solutions are then obtained using an adaptive shooting scheme together with the multi-region matching technique in the radial direction. Accordingly, the adaptive toroidal equilibrium (ATEQ) code is constructed for axisymmetric equilibrium studies. The adaptive numerical scheme in the radial direction improves considerably the accuracy of the equilibrium solution. The decomposition with independent solutions effectively reduces the matrix size in solving the magnetohydrodynamic equilibrium problem. The reduction of the matrix size is about an order of magnitude as compared with the conventional radially grid-based numerical schemes. Also, in this ATEQ numerical scheme, no matter how accuracy in the radial direction is imposed, the size of matrices basically does not change. The small matrix size scheme gives ATEQ more flexibility to address the requirement of the number of Fourier components in the poloidal direction in tough equilibrium problems. These two unique features, the adaptive shooting and small matrix size, make ATEQ useful to improve tokamak equilibrium solutions.
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40

Prevost, Paoline, Kristel Chanard, Luce Fleitout, Eric Calais, Damian Walwer, Tonie van Dam, and Michael Ghil. "Data-adaptive spatio-temporal filtering of GRACE data." Geophysical Journal International 219, no. 3 (September 19, 2019): 2034–55. http://dx.doi.org/10.1093/gji/ggz409.

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SUMMARY Measurements of the spatio-temporal variations of Earth’s gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission have led to new insights into large spatial mass redistribution at secular, seasonal and subseasonal timescales. GRACE solutions from various processing centres, while adopting different processing strategies, result in rather coherent estimates. However, these solutions also exhibit random as well as systematic errors, with specific spatial patterns in the latter. In order to dampen the noise and enhance the geophysical signals in the GRACE data, we propose an approach based on a data-driven spatio-temporal filter, namely the Multichannel Singular Spectrum Analysis (M-SSA). M-SSA is a data-adaptive, multivariate, and non-parametric method that simultaneously exploits the spatial and temporal correlations of geophysical fields to extract common modes of variability. We perform an M-SSA analysis on 13 yr of GRACE spherical harmonics solutions from five different processing centres in a simultaneous setup. We show that the method allows us to extract common modes of variability between solutions, while removing solution-specific spatio-temporal errors that arise from the processing strategies. In particular, the method efficiently filters out the spurious north–south stripes, which are caused in all likelihood by aliasing, due to the imperfect geophysical correction models and low-frequency noise in measurements. Comparison of the M-SSA GRACE solution with mass concentration (mascons) solutions shows that, while the former remains noisier, it does retrieve geophysical signals masked by the mascons regularization procedure.
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41

Kilinc, Muslum, and Juan M. Caicedo. "Finding Plausible Optimal Solutions in Engineering Problems Using an Adaptive Genetic Algorithm." Advances in Civil Engineering 2019 (February 27, 2019): 1–9. http://dx.doi.org/10.1155/2019/7475156.

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In engineering, optimization applications are commonly used to solve various problems. As widely known, solution of an engineering problem does not have a unique result; moreover, the solution of a unique problem may totally differ from one engineer to another. On the other hand, one of the most commonly used engineering optimization methods is genetic algorithm that leads us to only one global optimum. As to mention, engineering problems can conclude in different results from the point of different engineers’ views. In this study, a modified genetic algorithm named multi-solution genetic algorithm (MsGA) based on clustering and section approaches is presented to identify alternative solutions for an engineering problem. MsGA can identify local optima points along with global optimum and can find numerous solution alternatives. The reliability of MsGA was tested by using a Gaussian and trigonometric function. After testing, MsGA was applied to a truss optimization problem as an example of an engineering optimization problem. The result obtained shows that MsGA is successful at finding multiple plausible solutions to an engineering optima problem.
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42

Zhang, Qi, and Jiaqiao Hu. "Simulation Optimization Using Multi-Time-Scale Adaptive Random Search." Asia-Pacific Journal of Operational Research 36, no. 06 (December 2019): 1940014. http://dx.doi.org/10.1142/s0217595919400141.

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We propose a random search algorithm for seeking the global optimum of an objective function in a simulation setting. The algorithm can be viewed as an extension of the MARS algorithm proposed in Hu and Hu (2011) for deterministic optimization, which iteratively finds improved solutions by modifying and sampling from a parameterized probability distribution over the solution space. However, unlike MARS and many other algorithms in this class, which are often population-based, our method only requires a single candidate solution to be generated at each iteration. This is primarily achieved through an effective use of past sampling information by means of embedding multiple nested stochastic approximation type of recursions into the algorithm. We prove the global convergence of the algorithm under general conditions and discuss two special simulation noise cases of interest, in which we show that only one simulation replication run is needed for each sampled solution. A preliminary numerical study is also carried out to illustrate the algorithm.
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Wang, Shiqi, Zepeng Shen, and Yao Peng. "Hybrid Multi-Population and Adaptive Search Range Strategy With Particle Swarm Optimization for Multimodal Optimization." International Journal of Swarm Intelligence Research 12, no. 4 (October 2021): 146–68. http://dx.doi.org/10.4018/ijsir.2021100108.

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This paper proposes an algorithm named hybrid multi-population and adaptive search range strategy with particle swarm optimization (ARPSO) for solving multimodal optimization problems. The main idea of the algorithm is to divide the global search space into multiple sub-populations searching in parallel and independently. For diversity increasing, each sub-population will continuously change the search area adaptively according to whether there are local optimal solutions in its search space and the position of the global optimal solution, and in each iteration, the optimal solution in this area will be reserved. For the purpose of accelerating convergence, at the global and local levels, when the global optimal solution or local optimal solution is found, the global search space and local search space will shrink toward the optimal solution. Experiments show that ARPSO has unique advantages for solving multi-dimensional problems, especially problems with only one global optimal solution but multiple local optimal solutions.
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Leyland, Pénélope, Angelo Casagrande, and Yannick Savoy. "Parallel Mesh Adaptive Techniques Illustrated with Complex Compressible Flow Simulations." Modelling and Simulation in Engineering 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/317359.

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The aim of this paper is to discuss efficient adaptive parallel solution techniques on unstructured 2D and 3D meshes. We concentrate on the aspect of parallel a posteriori mesh adaptation. One of the main advantages of unstructured grids is their capability to adapt dynamically by localised refinement and derefinement during the calculation to enhance the solution accuracy and to optimise the computational time. Grid adaption also involves optimisation of the grid quality, which will be described here for both structural and geometrical optimisation.
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45

Fattah, Salmah, Ismail Ahmedy, Mohd Yamani Idna Idris, and Abdullah Gani. "Hybrid multi-objective node deployment for energy-coverage problem in mobile underwater wireless sensor networks." International Journal of Distributed Sensor Networks 18, no. 9 (September 2022): 155013292211235. http://dx.doi.org/10.1177/15501329221123533.

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Underwater wireless sensor networks have grown considerably in recent years and now contribute substantially to ocean surveillance applications, marine monitoring and target detection. However, the existing deployment solutions struggle to address the deployment of mobile underwater sensor nodes as a stochastic system. The system faces internal and external environment problems that must be addressed for maximum coverage in the deployment region while minimizing energy consumption. In addition, the existing traditional approaches have limitations of improving simultaneously the objective function of network coverage and the dissipated energy in mobility, sensing and redundant coverage. The proposed solution introduced a hybrid adaptive multi-parent crossover genetic algorithm and fuzzy dominance-based decomposition approach by adapting the original non-dominated sorting genetic algorithm II. This study evaluated the solution to substantiate its efficacy, particularly regarding the nodes’ coverage rate, energy consumption and the system’s Pareto optimal metrics and execution time. The results and comparative analysis indicate that the Multi-Objective Optimisation Genetic Algorithm based on Adaptive Multi-Parent Crossover and Fuzzy Dominance (MOGA-AMPazy) is a better solution to the multi-objective sensor node deployment problem, outperforming the non-dominated sorting genetic algorithm II, SPEA2 and MOEA/D algorithms. Moreover, MOGA-AMPazy ensures maximum global convergence and has less computational complexity. Ultimately, the proposed solution enables the decision-maker or mission planners to monitor effectively the region of interest.
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46

Gordon, D. F. "Asimovian Adaptive Agents." Journal of Artificial Intelligence Research 13 (September 1, 2000): 95–153. http://dx.doi.org/10.1613/jair.720.

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The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results.
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Kurzadkar, Prof S. "Adaptive Mechanism to Provide Medical Solution to Animals." International Journal for Research in Applied Science and Engineering Technology 9, no. 1 (January 31, 2021): 689–93. http://dx.doi.org/10.22214/ijraset.2021.32827.

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Chen, Baozhi, Parul Pandey, and Dario Pompili. "An Adaptive Sampling Solution using Autonomous Underwater Vehicles." IFAC Proceedings Volumes 45, no. 27 (2012): 352–56. http://dx.doi.org/10.3182/20120919-3-it-2046.00060.

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49

Ilinca, F., D. Pelletier, and L. Ignat. "Adaptive Finite Element Solution of Compressible Turbulent Flows." AIAA Journal 36, no. 12 (December 1998): 2187–94. http://dx.doi.org/10.2514/2.343.

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Stevenson, Rob. "Adaptive Solution of Operator Equations Using Wavelet Frames." SIAM Journal on Numerical Analysis 41, no. 3 (January 2003): 1074–100. http://dx.doi.org/10.1137/s0036142902407988.

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