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A fast method for solving second order boundary value Volterra integro-differential equations

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 1997年第1-2期65卷 121-129页

作者： Shaw, RE Garey, LE Univ New Brunswick St John NB E2L 4L5 Canada

Using finite difference methods to solve nonlinear Volterra integro-differential equations with two point boundary conditions give rise to a symmetric banded coefficient matrix. A typical method for solving systems of this form involves the LU method. In this paper the original system is modified to allow the implementation of a special fast algorithm for solving tridiagonal systems. Numerical examples are given to compare an efficient form of the LU method with the new approach.

关键词： algorithm efficiency difference methods Volterra integro-differential equations

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Simulation of miscible flow including bypassed oil and dispersion control

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SPE RESERVOIR EVALUATION & ENGINEERING 2007年第5期10卷 500-507页

作者： Coats, K. H. Thomas, L. K. Pierson, R. G. Coats Engineering ConocoPhillips (retired)

This paper describes methods for the simulation of first-contact miscible (FCM), multicontact miscible, and vaporizing gas drive, which include logic to account for bypassed oil. Dispersion control is included for FCM cases. First-contact miscibility may be simulated in fully compositional mode with any number of components, N-c, or in two-pseudocomponent equation-of-state (EOS) mode. Pseudoization is performed internally in the model so that hydrocarbon-fluid density and viscosity, as functions of pressure and composition, are the same as calculated in N-c-component mode. Bypassed oil and dispersion control are based on an extension of Koval's (1963) method. This procedure allows the user to adjust the fractional flow of oil during upscaling or history matching to match fine-grid or historical results. This feature can be used to simulate both water-alternating-gas (WAG) and tertiary-recovery projects where solvent injection is preceded by a water-injection period. Example cases are included to illustrate the techniques presented in this paper. Results are also given on the efficiencies of the algorithms.

关键词： GAS DRIVE simulating algorithm efficiency oils Formal logic SOLVENT INJECTION

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Dynamic Analysis Techniques for Road Bridges Under Large Traffic Flows

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2024年第0期

作者： Camara, Alfredo Wang, Jie Goicolea, Jose M. Univ Politecn Madrid Dept Continuum Mech & Theory Struct Calle Prof Aranguren S-N Madrid 28040 Spain

This work focuses on the dynamic analysis of road bridges under large microsimulated traffic flows, contributing an efficient dynamic analysis framework for future fatigue, comfort or driving safety studies in which realistic interactions between vehicles are considered. Different strategies to define the loading vector in the governing dynamic equations are presented for bridges discretized with beam and shell elements. The proposed interpolation techniques for beam-like models include a recursive binary search and a novel vectorized strategy based on shape functions expanded to the entire length of the deck. These are applied to a short simply supported bridge and a very long cable-stayed bridge under traffic flows generated with a cellular automata microsimulation model. The strategies presented here reduce the computational time required in conventional load interpolation algorithms, particularly in long bridges subject to dense traffic flows. Finally, the binary search algorithm is applied to the study of bridge decks discretized with shell elements under microsimulated traffic, demonstrating that high vehicle densities increase the contribution of the transverse vibration of the slab and the potential discomfort of pedestrians.

关键词： Microsimulated traffic bridge dynamics load interpolation algorithm efficiency

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Examining applying high performance genetic data feature selection and classification algorithms for colon cancer diagnosis

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017年 146卷 11-24页

作者： Al-Rajab, Murad Lu, Joan Xu, Qiang Univ Huddersfield Huddersfield W Yorkshire England

Background and Objectives: This paper examines the accuracy and efficiency (time complexity) of high performance genetic data feature selection and classification algorithms for colon cancer diagnosis. The need for this research derives from the urgent and increasing need for accurate and efficient algorithms. Colon cancer is a leading cause of death worldwide, hence it is vitally important for the cancer tissues to be expertly identified and classified in a rapid and timely manner, to assure both a fast detection of the disease and to expedite the drug discovery process. Methods: In this research, a three-phase approach was proposed and implemented: Phases One and Two examined the feature selection algorithms and classification algorithms employed separately, and Phase Three examined the performance of the combination of these. Results: It was found from Phase One that the Particle Swarm Optimization (PSO) algorithm performed best with the colon dataset as a feature selection (29 genes selected) and from Phase Two that the Support Vector Machine (SVM) algorithm outperformed other classifications, with an accuracy of almost 86%. It was also found from Phase Three that the combined use of PSO and SVM surpassed other algorithms in accuracy and performance, and was faster in terms of time analysis (94%). Conclusions: It is concluded that applying feature selection algorithms prior to classification algorithms results in better accuracy than when the latter are applied alone. This conclusion is important and significant to industry and society. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Colon cancer algorithm efficiency Feature selection Classification Gene Expression

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Reducing the Label Space a Predefined Ratio for a More Efficient Multilabel Classification

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IEEE ACCESS 2022年 10卷 76480-76492页

作者： Moyano, Jose M. Luna, Jose M. Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal Andalusian Res Inst Data Sci & Computat Intellige Cordoba 14071 Spain

The multi-label classification task has been widely used to solve problems where each of the instances may be related not only to one class but to many of them simultaneously. Many of these problems usually comprise a high number of labels in the output space, so learning a predictive model from such datasets may turn into a challenging task since the computational complexity of most algorithms depends on the number of labels. In this paper, we propose a methodology to reduce the label space a user predefined ratio of labels, aiming to improve the runtime of the multi-label classification algorithms. Obviously, such reduction should be done without producing a significant drop in their final predictive performance. The experimental analysis carried out over 25 well-known multi-label datasets, demonstrates a drastic reduction in the runtime. Besides, it is statistically proven that reducing 20% the number of labels does not lead to a decrease in the predictive performance of the multi-label algorithms using four well-known evaluation measures. Even more, in many cases, although reductions of up to 50% of the output space are made, the predictive performance of the algorithms is not significantly different from using the whole set of labels.

关键词： Prediction algorithms Classification algorithms Task analysis Runtime Proposals Focusing Training algorithm efficiency binary classification dimensionality reduction label space reduction multi-label classification

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The Fast and the Robust: Trade-Offs Between Optimization Robustness and Cost in the Calibration of Environmental Models

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WATER RESOURCES RESEARCH 2018年第11期54卷 9432-9455页

作者： Kavetski, Dmitri Qin, Youwei Kuczera, George Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA Australia Univ Newcastle Sch Engn Callaghan NSW Australia Hohai Univ Ctr Global Change & Water Cycle State Key Lab Hydrol Water Resources & Hydraul En Nanjing Jiangsu Peoples R China

Environmental modelers using optimization algorithms for model calibration face an ambivalent choice. Some algorithms, for example, Newton-type methods, are fast but struggle to consistently find global parameter optima;other algorithms, for example, evolutionary methods, boast better global convergence but at much higher cost (e.g., requiring more objective function calls). Trade-offs between accuracy/robustness versus cost are ubiquitous in numerical computation, yet environmental modeling studies have lacked a systematic framework for quantifying these trade-offs. This study develops a framework for benchmarking stochastic optimization algorithms in the context of environmental model calibration, where multiple algorithm invocations are typically necessary. We define reliability as the probability of finding the desired (global or tolerable) optimum from random initial points and estimate the number of invocations to find the desired optimum with prescribed confidence (here 95%). A robust algorithm should achieve consistently high reliability across many problems. A characteristic efficiency metric for algorithm benchmarking is defined as the total cost (objective function calls over multiple invocations) to find the desired optimum with prescribed confidence. This approach avoids the pitfalls of existing approaches that compare costs without controlling the confidence in algorithm success. A case study illustrates the framework by benchmarking the Levenberg-Marquardt and Shuffled Complex Evolution (SCE) algorithms over three catchments and four hydrological models. In 8 of 12 scenarios, Levenberg-Marquardt is more efficient than SCEby sacrificing some of its speed advantage to match SCE reliability through more invocations. The proposed framework is easy to apply and can help guide algorithm selection in environmental model calibration.

关键词： optimization benchmarking model calibration performance trade-offs algorithm efficiency robustness multistart optimization

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Optimization of batch and fed-batch bioreactors using simulated annealing

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BIOTECHNOLOGY PROGRESS 2004年第4期20卷 1285-1288页

作者： Kookos, IK UMIST Dept Chem Engn Manchester M60 1QD Lancs England

In order for biobased industrial products to compete economically with petroleum-derived products, significant reduction in their processing cost is necessary. Since most bioprocesses are operated in batch or fed-batch mode, their optimization involves theoretical and computational challenges. Simulated annealing (SA), a stochastic optimization algorithm, is used in this study to solve a number of challenging optimization problems related to the design and operation of bioreactors. Two well-known case studies are considered in which the robustness and efficiency of the SA algorithm is demonstrated. More specifically, in the first case study it is shown that the global optimal solution located by SA achieves significant improved productivity when compared with the results of previous investigations. In the second case study a realistic objective function is considered where the economic performance of a bioprocess is optimized. SA exhibits impeccable performance and robustness and was able to locate the global optimal solution irrespective of the initial point selected.

关键词： BIOPROCESSING simulated annealing zero point BATCHES global optimum Case studies Bioreactors algorithm efficiency Robustness

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Acceleration Strategies Based on an Improved Bubble Packing Method

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Communications in Computational Physics 2014年第6期16卷 115-135页

作者： Nan Qi Yufeng Nie Weiwei Zhang Department of Applied Mathematics School of ScienceNorthwestern Polytechnical University127 Youyi West RoadXi’an 710129China.

The bubble packing method can generate high-quality node sets in simple and complex ***,its efficiency remains to be *** study is a part of an ongoing effort to introduce several acceleration schemes to reduce the cost of ***,allow the viscosity coefficient c in the bubble governing equations to change according the coordinate of the bubble which are defined separately as odd and normal bubbles,and meanwhile with the saw-shape relationship with time or ***,in order to relieve the over crowded initial bubble placement,two coefficients w1 and w2 are introduced to modify the insertion *** range of those two coefficients are discussed to be w1=1,w2∈[0.5,0.8].Finally,a self-adaptive termination condition is logically set when the stable system equilibrium is *** examples illustrate that the computing cost can significantly decrease by roughly 80%via adopting various combination of proper schemes(except the uniform placement example),and the average qualities of corresponding Delaunay triangulation substantially exceed *** shows that those strategies are efficient and can generate a node set with high quality.

关键词： Bubble packing method algorithm efficiency viscosity coefficient mesh generation Delaunay triangulation.

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Instance scale, numerical properties and design of metaheuristics: A study for the facility location problem

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IFAC-PapersOnLine 2019年第13期52卷 2219-2224页

作者： Chalupa, David Nielsen, Peter Operations Research Group Department of Materials and Production Aalborg University Fibigerstræde 16 Aalborg9220 Denmark

We present an in-depth computational study of two local search metaheuristics for the classical uncapacitated facility location problem. We investigate four problem instance models, studied for the same problem size, for which the two metaheuristics exhibit intriguing and contrasting behaviours. The metaheuristics explored include a local search (LS) algorithm that chooses the best moves in the current neighbourhood, while a randomised local search (RLS) algorithm chooses the first move that does not lead to a worsening. The experimental results indicate that the right choice between these two algorithms depends heavily on the distribution of coefficients within the problem instance. This is also put further into context by finding optimal or near-optimal solutions using a mixed-integer linear programming problem solver. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Integer programming Combinatorial optimization Heuristic algorithms Local search (optimization) Location algorithm efficiency Facility location problem Integer Linear Programming Local search Local search algorithm Local search metaheuristics Mixed integer linear programming problems Uncapacitated facility locations

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Convergence of the modified fixed-point method in magnetic field problems with hysteresis

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EUROPEAN PHYSICAL JOURNAL-APPLIED PHYSICS 1999年第1期5卷 63-69页

作者： Ossart, E Ionita, V ENSIEG Lab Electrotech Grenoble F-38402 St Martin Dheres France Univ Politehn Bucuresti Bucharest Romania

The paper presents a fixed point iterative method for the magnetic field computation in hysteretic media. The method is applied to a 2D field computation by the finite element method, hysteresis being modeled by a simple analytical scalar model. Convergence of the computation is verified. A test problem, unsolved by classical methods, is treated using both the scalar and vector magnetic potentials. The obtained results show the algorithm efficiency, which is then applied to magnetic recording modeling.

关键词： Scalar Magnetic recording test problem magnetic potential Finite element method algorithm efficiency Hysteresis iterative methods field calculation

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