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检索条件"任意字段=2016 International Congress on Computation Algorithms in Engineering"

共 1728 条记录，以下是111-120 订阅

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EFFICIENT PARALLEL SCALABLE MATRIX-FREE 3D HIGH-ORDER FINITE ELEMENT SIMULATION OF NEO-HOOKEAN COMPRESSIBLE HYPERELASTICITY AT FINITE STRAIN

EFFICIENT PARALLEL SCALABLE MATRIX-FREE 3D HIGH-ORDER FINITE...

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ASME international Mechanical engineering congress and Exposition (IMECE)

作者： Mehraban, Arash Brown, Jed Tufo, Henry Thompson, Jeremy Shakeri, Rezgar Regueiro, Richard Univ Colorado Dept Comp Sci Boulder CO 80309 USA Univ Colorado Dept Appl Math Boulder CO 80309 USA Univ Colorado Dept Civil Environm & Architectural Engn Boulder CO 80309 USA

ISBN: (纸本)9780791885680

The paper investigates matrix-free high-order implementation of finite element discretization with p-multigrid preconditioning for the compressible Neo-Hookean hyperelasticity problem at finite strain on unstructured 3D meshes in parallel. We consider two formulations for the matrix-free action of the Jacobian in Neo-Hookean hyperelasticity: (i) working in the reference configuration to define the second Piola-Kirchhoff tensor as a function of the Green-Lagrange strain S(E) (or equivalently, the right Cauchy-Green tensor C = 1+2E), and (ii) working in the current configuration to define the Kirchhoff stress in terms of the left Cauchy-Green tensor tau(b). The proposed efficient algorithm utilizes the Portable, Extensible Toolkit for Scientific computation (PETSc), along with the libCEED library for efficient compiler optimized tensor-product-basis computation to demonstrate an efficient nonlinear solution algorithm. We utilize p-multigrid preconditioning on the high-order problem with algebraic multigrid (AMG) on the assembled linear Q(1)coarse grid operator. In contrast to classical geometric multigrid, also known as h-multigrid, each level in p-multigrid is related to a different approximation polynomial order p, instead of the element size h. A Chebyshev polynomial smoother is used on each multigrid level. AMG is then applied to the assembled Q (trilinear hexahedral elements), which allows low storage that can be efficiently used to accelerate convergence to a solution. For the compressible Neo-Hookean hyperelastic constitutive model we exploit the stored energy density function to compute the stored elastic energy density of the Neo-Hookean material as it relates to the deformation gradient. Based on our formulation, we consider four different algorithms each with different storage strategies. algorithms 1 and 3 are implemented in the reference and current configurations respectively and store del(X)xi, det(del X-xi), and del(X)u. Algorithm 2 in the reference co

关键词： Simulation Finite element analysis algorithms Polynomials Tensors Deformation Storage computation Computer software Aluminum Density Constitutive equations Energy storage Stress Poisson ratio Elastic moduli Jacobian matrices Algebra Tradeoffs Approximation

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Dynamic Multi-Workflow Scheduling: A Comparative Analysis of Real-time Data 13

Dynamic Multi-Workflow Scheduling: A Comparative Analysis of...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Rathi, Sugandha Mehrotra, Deepti Nagpal, Renuka Srivastava, Gautam Amity University Department of Computer Science and Engineering Noida India Amity University Department of Information Technology Noida India Brandon University Department of Math and Computer Science Canada Lebanese American University Department of Computer Science and Math Beirut1103 Lebanon

ISBN: (纸本)9798350308365

Fog computing stands as a recent and significant technological advancement in the realm of computing technologies, addressing several limitations inherent in cloud computing and yielding diverse optimizations. As the demand for the payper-use model rises, dynamic workflow scheduling has emerged as a prominent trend for handling incoming workflow tasks. This paper shows a comparative analysis of three optimization algorithms - Genetic Algorithm(GA), Particle Swarm Optimization(PSO), and Grey Wolf Optimization(GWO) - with a focus on two crucial parameters: total cost and resource utilization. The study employs dynamic datasets to ensure the practicality and applicability of the results across various problem domains, ultimately contributing to their optimization. The findings of the comparative analysis are thoroughly examined and discussed in the results section. © 2024 IEEE.

关键词： Scheduling algorithms

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A comparative study of differential evolution algorithms for parameter fitting procedures

A comparative study of differential evolution algorithms for...

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2016 IEEE congress on Evolutionary computation, CEC 2016

作者： Torres-Cerna, Carlos E. Alanis, Alma Y. Poblete-Castro, Ignacio Bermejo-Jambrina, Marta Hernandez-Vargas, Esteban A. CUCEI Universidad de Guadalajara Blvd. Marcelino Garcia Barragan Col. Olímpica Zapopan JaliscoC.P. 44330 Mexico Biosystems Engineering Laboratory Center for Bioinformatic and Integrative Biology Universidad Andrés Bello Republica 239 Santiago Chile Systems Medicine of Infectious Diseases Helmholtz Centre for Infection Research Inhoffenstr.7 Braunschweig38124 Germany

ISBN: (纸本)9781509006229

Parameter fitting consists on the estimation of model parameters using experimental data from the studied process, which can be considered as a nonlinear optimization problem. In this sense, evolutionary computation has shown its great capability to solve multimodal nonlinear optimization problems. This paper compares different variants of the Differential Evolution (DE) algorithm to minimize the residual sum of squares between the outcome of the mathematical model and experimental data. To compare the different variants of the DE algorithm, a biopolymer production model is considered. Simulations results suggest a trend for the best fit using the DE/best/ variants. However, the DE/rand/ variant provides more stable results respect to the average and standard deviation of different trials. Finally, the biopolymer production problem is discussed. © 2016 IEEE.

关键词： Parameter estimation

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OPTIMIZING CONTROL SYSTEMS: A COMPARATIVE ANALYSIS OF 77 MULTI-OBJECTIVE EVOLUTIONARY algorithms

OPTIMIZING CONTROL SYSTEMS: A COMPARATIVE ANALYSIS OF 77 MUL...

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ASME 2024 international Mechanical engineering congress and Exposition, IMECE 2024

作者： AbuKwaik, Mohammad Sardahi, Yousef Alshorman, Ahmad M. Irbid Jordan Mechanical Engineering Department Marshall University West Virginia United States Mechanical Engineering Department JUST Irbid Jordan

ISBN: (纸本)9780791888636

Multi-objective optimization is integral to control systems, particularly in engineering, where multiple objectives must be concurrently satisfied. This study pioneers a comprehensive comparison of 77 evolutionary algorithms for optimizing a second-order system under a proportional-integral-derivative (PID) controller. We emphasize the challenges of balancing conflicting metrics like peak time and percentage overshoot and how PID controller tuning is crucial in achieving this balance. To assess the algorithms’ performance, we utilize four different performance metrics, which encompass Generational Distance (GD), Spacing, Spread, and computation time. The optimization results are analyzed using two methods. Firstly, after 1 million iterations, outcomes are visually represented through discrete charts for each algorithm, detailing their performance metrics. The Multiple single objective Pareto sampling II (MSOPSII) algorithm demonstrates the most effective distribution of the solution set and maintains uniformity well. The Indicator-Based Evolutionary Algorithm (IBEA) excels in the Spacing Metric, while the Speed-constrained Multi-objective Particle Swarm Optimization (SMPSO) algorithm exhibits superior convergence. Secondly, algorithms with optimal performance are evaluated by considering multiple metrics simultaneously. The theta-Dominance-based Evolutionary Algorithm (tDEA) leads in combined convergence and spread, Multi-Tasking Constrained Multi-objective Optimization (MTCMO) stands out for convergence and spacing, and MSOPSII and IBEA perform best in spread and spacing. Across all three metrics, MOEA/D with Adaptive Weight Adjustment (MOEADAWA) emerges as the top performer. These outcomes not only advance our understanding of algorithmic efficiency in multi-objective scenarios but also provide valuable insights for the application of these algorithms in practical control system challenges. This research fills a critical gap in existing literature, offering an in-

关键词： Constrained optimization

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Triangle Search Optimization Algorithm for Single-Objective Bound-Constrained Numerical Optimization 5

Triangle Search Optimization Algorithm for Single-Objective ...

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5th international Conference on Mechanical, Control and Computer engineering (ICMCCE)

作者： Wei, Zhenglei Tang, Shangqin Xie, Lei Tang, Andi Li, Yintong Zhang, Peng Air Force Engn Univ Inst Aeronaut Engn Xian Peoples R China

ISBN: (纸本)9781665423144

Real-parameter optimization has been a focus of the last decade. An inspired algorithm, triangle search optimization (TSO), is proposed for the congress on Evolutionary computation (CEC) 2020 competition. In this paper, the TSO algorithm is divided into two phases: the triangle vertex searching (TVS) and triangle edge searching (TES) phases. In the TVS phase, the population is divided into two subpopulations, which can be enhanced by vertex searching operators and the covariance matrix adaptation evolution strategy (CMA-ES) for exploitation. In the TES phase, the differential evolution vector between superior and inferior solutions is employed to improve the exploration ability. The TSO algorithm is evaluated on the CEC 2020 benchmark problems and compared with the winners of the CEC competition and novel competitors. The experimental results show that TSO outperforms the compared algorithms.

关键词： CEC2020 Triangle search optimization evolutionary computation numerical optimization

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Enhanced computation of the EEG-IC Polarities using a Genetic Algorithm 12

Enhanced Computation of the EEG-IC Polarities using a Geneti...

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12th international congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Munilla, Jorge Ortiz, Andres Alsafi, Haedar E.S. Luque, Juan L. Universidad de Málaga Department of Communication Engineering Málaga Spain Universidad de Málaga Department of Developmental Psychology and Education Málaga Spain

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

Electroencephalogram (EEG) is nowadays an essential tool for clinical and cognitive brain research. ICA algorithms are applied to EEG recordings to find maximally temporally independent statistical source signals, which are further referred to as independent components (ICs) and which represent brain and non-brain processes. Clustering of IC topographies is a popular way to assess the results of EEG-based experiments and usually implies the computation of average images of such IC topographies. This computation, however, requires a previous process to determine the polarity of the different ICs because component scalp maps have not absolute polarity. This sign ambiguity problem is solved in the bibliography by using an image as a reference and setting the polarity of each IC according to the sign of the correlation of this IC with such reference. This method, however, has issues with low values of correlation. Thus, this paper introduces a different approach that addresses the polarity computation from a more general view, trying to find the polarity vector that minimizes the global error, and proposes the use of a genetic algorithm to find it. The results obtained with this approach outperform the two main methods currently used. © 2022 IEEE.

关键词： Genetic algorithms

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HIGH SPEED computation OF OPTIMUM CONTROLS.

HIGH SPEED COMPUTATION OF OPTIMUM CONTROLS.

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ICAS Proceedings 1984, 14th congress of the international Council of the Aeronautical Sciences.

作者： Matsouka, H. Univ of Tokyo Faculty of Engineering Tokyo Jpn Univ of Tokyo Faculty of Engineering Tokyo Jpn

ISBN: (纸本)0915928892

In case of calculating the optimizing control signals derived from the maximum principle of L. S. Pontryagin, based on an iterative algorithm due to L. W. Neustadt, required for solving a two-point boundary value problem, the iterative procedure is extremely sensitive to computational error, so that the convenient digital hybrid system composed of a digital differential analyzer and a general-purpose digital computer was experimentally organized for the on-line study of a rapid convergence with high precision. Results of the experimental digital hybrid implementation with somewhat refined algorithms show that the rapid convergence is practically possible for the 2nd- and 3rd-order system, and the 2nd-order system subject to energy constraints. Convergence processes are displayed graphically. (Author abstract. )

关键词： AIRCRAFT

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A detection algorithm of ballast track fastener loss based on shooting direction 9

A detection algorithm of ballast track fastener loss based o...

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9th international congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2016

作者： Wang, Mingyu Zhang, Xinfeng College of Electronic and Control Engineering Beijing University of Technology Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing Laboratory for Urban Mass Transit Beijing China

ISBN: (纸本)9781509037100

In this paper, we propose a new positioning and automatic detection algorithm of ballast track fastener missing, which is based on different shooting direction. The crossover method based on Fast-Kirsch edge detection arithmetic and HOUGH straight line detection is presented for the direction-based positioning algorithm. It has very strong transplantability without the effect of shadow, inclination of track, obstacles and in relatively accurate location areas for missing fastener scanning, FAST-PCA is used as the feature extraction method of identification after the comparison of several algorithms. © 2016 IEEE.

关键词： Edge detection

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REFINEMENT AND SCALING EFFECTS ON PERIDYNAMIC NUMERICAL SOLUTIONS

REFINEMENT AND SCALING EFFECTS ON PERIDYNAMIC NUMERICAL SOLU...

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ASME international Mechanical engineering congress and Exposition (IMECE2016)

作者： Dipasquale, Daniele Oterkus, Erkan Sarego, Giulia Zaccariotto, Mirco Galvanetto, Ugo Univ Padua Dept Ind Engn Via Venezia 1 Padua Italy Univ Strathclyde Dept Naval Architecture Ocean & Marine Engn Glasgow Lanark Scotland

ISBN: (纸本)9780791850510

One of the most common methods to implement peridynamics numerically is based on the discretization of the whole body by means of a structured and regular grid of nodes and a constant horizon size. That leads to an inefficient use of computational resources as well as to the impossibility to explore the multi-scale capabilities of peridynamics within a unique framework. Adaptive grid refinement and scaling seem to be a promising strategy to reduce those limitations, allowing to increase the resolution of the analysis and to reach the interested length-scale only in the desired regions. The application of such an approach in the peridynamic solutions requires certainly to be investigated, in particular, this is done by the comparison of numerical peridynamic solutions with the analytical solutions of classic linear elasticity theory.

关键词： computation theory

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Surrogate-Assisted Multi-Objective Optimization for Handling Objectives with Heterogeneous Evaluation Times: Unconstrained Problems 13

Surrogate-Assisted Multi-Objective Optimization for Handling...

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13th IEEE congress on Evolutionary computation, CEC 2024

作者： Santoshkumar, Balija Deb, Kalyanmoy Michigan State University Department of Mechanical Engineering East LansingMI48824 United States Michigan State University Department of Electrical and Computer Engineering East LansingMI48824 United States

ISBN: (纸本)9798350308365

Surrogates are commonly used in single and multi-objective optimization studies for quickly evaluating objective functions which are otherwise expensive to evaluate. Starting with a set of high-fidelity evaluated solutions, optimization algorithms generate new in-fill solutions for further high-fidelity evaluations to improve the previous surrogate models towards the optimal regions of the search space. In terms of evaluating in-fill solutions, most current optimization algorithms must evaluate all objectives using high-fidelity means, irrespective of relative differences in their evaluation times. In this paper, we address the issue of handling objective functions having heterogeneous high-fidelity evaluation times and propose a framework which combines evaluation time and surrogate accuracy of each objective to decide which population members should undergo a high-fidelity evaluation and for which objectives. Our proposed approach deals with heterogeneously evaluated solutions - some objectives with high-fidelity and some with surrogates - in a generic way. Initial results on a number of two and three-objective problems, presented in this paper, show promise for approach and encourages to launch a deeper study on more complex and constrained problems. © 2024 IEEE.

关键词： Multiobjective optimization

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