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检索条件"主题词=Optimization algorithms"

共 3923 条记录，以下是111-120 订阅

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A Comprehensive Survey on Dimension Reduction for Medical Big Data Using optimization algorithms 4

A Comprehensive Survey on Dimension Reduction for Medical Bi...

引用

4th International Mobile, Intelligent, and Ubiquitous Computing Conference

作者： Abdelminaam, Diaa Salama Abu Alheijat, Ahmed Taha, Mohamed Benha Univ Fac Comp & Artificial Intelligence Banha Egypt Misr Int Univ Fac Comp Sci Cairo Egypt Obour High Inst Informat Dept Comp Sci Cairo Egypt

ISBN: (纸本)9798350367782;9798350367775

This paper surveys dimension reduction techniques in medical big data using optimization algorithms to address challenges like computational inefficiency, overfitting, and interpretability in high-dimensional datasets. As medical data from sources like electronic health records, genomics, and imaging grow, efficient processing is essential for personalized healthcare. The paper explores feature extraction (PCA, LDA) and feature selection methods, emphasizing metaheuristic algorithms like Genetic algorithms (GA), Particle Swarm optimization (PSO), and Ant Colony optimization (ACO). These algorithms enhance machine learning model accuracy by selecting relevant features, reducing computational costs, and handling nonlinear relationships in medical data. Applications in diagnosis, treatment prediction, and disease classification are discussed. Future research aims to integrate various optimization strategies and deep learning for more effective dimensionality reduction in healthcare.

关键词： Dimension Reduction Medical Big Data feature selection optimization algorithms machine learning Meta-heuristics

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On Decomposition and Convergence of Distributed optimization algorithms

On Decomposition and Convergence of Distributed Optimization...

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American Control Conference (ACC)

作者： Wu, Wuwei Zhang, Shiqi Li, Zhongkui Chen, Jie Georgiou, Tryphon T. City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China Peking Univ Dept Mech & Engn Sci Coll Engn Beijing 100871 Peoples R China Univ Calif Irvine Dept Mech & Aerosp Engn Irvine CA 92697 USA

ISBN: (纸本)9798350382662;9798350382655

This paper analyzes distributed optimization algorithms from a frequency-domain perspective. We propose a general class of gradient-based distributed algorithms that can be characterized as Lur'e systems, thereby enabling the analysis and synthesis of algorithms following a robust control approach facilitated by the Zames-Falb criterion. By identifying algorithmic convergence with the absolute stability of a corresponding Lur'e system and decomposing the optimization objective into two canonical control problems, namely tracking and servomechanism, the problem of optimizing convergence rate is recast as a Nevanlinna-Pick interpolation problem. The solutions to such analytic interpolation problems lead to a parameterization of distributed optimization algorithms that achieve specified convergence rates.

关键词： optimization algorithms Lur'e systems Nevanlinna-Pick interpolation convergence analysis

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Knowledge management in the development of optimization algorithms

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Symposium on Human Interface held at the HCI International 2007

作者： Crawford, Broderick Castro, Carlos Monfroy, Eric Pontificia Univ Catolica Valparaiso Valparaiso Chile

ISBN: (纸本)9783540733447

This paper captures our experience developing algorithms to solve Combinatorial Problems using different techniques. Because it is a Software Engineering problem, then to find better ways of developing algorithms, solvers and metaheuristics is our interest too. Here, we fixed some concepts from Knowledge Management and Software Engineering applied in our work.

关键词： knowledge management software engineering agile development creativity optimization algorithms

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On Comparing Six optimization algorithms for Network-based Wind Speed Forecasting 37

On Comparing Six Optimization Algorithms for Network-based W...

引用

37th Chinese Control Conference (CCC)

作者： Tao, Huanjie Lu, Xiaobo Southeast Univ Sch Automat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9789881563958

Network-based wind speed forecasting has played an important role in the power system. The network parameters optimization is an important issue, and different optimization algorithms are believed to result in different forecasting accuracies. In this paper, six network parameters optimization algorithms, including Gradient descent, Momentum, AdaGrad, RMSprop, Adam. and Adadelta, are implemented and compared in the application of wind speed forecasting. As a case study, this paper uses a wind speed data obtained from Ningxia, China. The performance is evaluated by three metrics, namely, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The experiment results show that, Adam algorithm and RMSprop algorithm achieve better forecasting accuracy and less training time than the other optimization algorithms. This study can be a guide to the selection of optimization algorithms on wind speed forecasting problems for researchers.

关键词： Wind speed forecasting Back-propagation neural network Forecasting accuracy optimization algorithms

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Optically assisted absolute positioning of robots in a workcell utilizing optimization algorithms 8

Optically assisted absolute positioning of robots in a workc...

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Conference on Optical Sensing and Detection VIII

作者： Ross, Tobias Lutter, Klaus Ennes, Christian Uphues, Thorsten Inst Sensor & Actuator Technol Hofbrauhaus 1b D-96450 Coburg Germany Coburg Univ Appl Sci Friedrich Streib Str 2 D-96450 Coburg Germany Brose Fahrzeugteile SE & Co KG Max Brose Str 1 D-96450 Coburg Germany

ISBN: (纸本)9781510673175;9781510673168

Unlike robots, humans have no problem working hand in hand. However, when robots try to perform tasks that require precision, they always face a major problem: they are not precise enough in combination. This problem is especially relevant when the application requires high precision. One of the main reasons for this is that the robots usually do not know each other's position or have an absolute calibration of the common coordinate system with the required accuracy. We will present our approach to introduce precise alignment procedures in a robotic cell using optically assisted methods. Subsequent analysis of the acquired data by tailored optimization algorithms provide an accurate and absolute coordinate system for a robot ensemble. The accuracy is typically limited by the workspace environment. Mostly acoustic vibrations will define the lower limit of the absolute precision to retrieve a global coordinate system within the working environment. In this work we present an analysis of common limitations as well as an algorithmic procedure to retrieve a global orthonormal basis for the robotic workcell, independent of the number of robots in the ensemble. As an intermediate result of our ongoing research, we can demonstrate a repeatable adjustment accuracy of less than 100 mu m euclidean distance from a common center.

关键词： optical metrology robotics absolute calibration optimization algorithms workcell

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Local search for parallel optimization algorithms for high diminsional optimization problems 22

Local search for parallel optimization algorithms for high d...

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22nd International Conference on Circuits, Systems, Communications and Computers (CSCC)

作者： Abd-Alsabour, Nadia Cairo Univ Cairo Egypt

Local search algorithms perform an important role when being employed with optimization algorithms tackling numerous optimization problems since they lead to getting better solutions. However, this is not practical in many applications as they do not contribute to the search process. This was not much studied previously for traditional optimization algorithms or for parallel optimization algorithms. This paper investigates this issue for parallel optimization algorithms when tackling high dimensional subset problems. The acquired results show impressive recommendations.

关键词： local search optimization algorithms paralell algorithms high dimensional subset problems

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Differential Evolution and Particle Swarm optimization algorithms with Two Stage Initialization for PID Controller Tuning in Coupled Tank Liquid Level System

Differential Evolution and Particle Swarm Optimization Algor...

引用

International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Puralachetty, Manasa Madhavi Pamula, Vinay Kumar JNTUK Univ Coll Engn Kakinada Dept Elect & Commun Engn Kakinada 533003 India

ISBN: (纸本)9781509033645

In this paper, a different type of initialization technique called the two stage initialization (TSI) is used for initializing the population vectors of differential evolution (DE) and particle swarm optimization (PSO). These two stage initialized optimization algorithms are then used to tune the PID controller for a coupled tank liquid level control system. In TSI, the population vector is randomly generated in two stages which would then go through the various phases involved in the algorithms. The PID controller is the most preferred controller in almost all process control industries due to its robustness and dynamic behavior. Hence it has been chosen for controlling the coupled tank liquid level system in this paper. Comparison of the application of four optimization algorithms out of which two are already existing ones and the other two are their TSI versions to PID-coupled tank liquid level system is also done. It can be found that TSI not only helps the existing optimization algorithms to start at a better optima but also to converge at a lesser minimum value of the objective function, which is often desirable.

关键词： optimization algorithms PID Controller Coupled tank liquid level control system Two stage initialization DE PSO

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Applications and Comparisons of optimization algorithms Used in Convolutional Neural Networks

Applications and Comparisons of Optimization Algorithms Used...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Seyyarer, Ebubekir Uckan, Taner Hark, Cengiz Ayata, Faruk Inan, Mevlut Karci, Ali Van Yuzuncu Yil Univ Bilgisayar Teknolojileri Bolumu Van Turkey Bitlis Eren Univ Bilgisayar Teknolojileri Bolumu Bitlis Turkey Inonu Univ Bilgisayar Muhendisligi Malatya Turkey

ISBN: (纸本)9781728129334;9781728129327

Nowadays, it is clear that the old mathematical models are incomplete because of the large size of image data set. For this reason, the Deep Learning models introduced in the field of image processing meet this need in the software field In this study, Convolutional Neural Network (CNN) model from the Deep Learning algorithms and the optimization algorithms used in Deep Learning have been applied to international image data sets. optimization algorithms were applied to both datasets respectively, the results were analyzed and compared The success rate was approximately 96.21% in the Caltech 101 data set, while it was observed to be approximately 10% in the Cifar-100 data set.

关键词： Deep learning Convolutional neural networks optimization algorithms Caltech 101 Cifar-100

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Exploring the Application of Multi-Objective optimization algorithms in Logistics Sensor Networks 2

Exploring the Application of Multi-Objective Optimization Al...

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2nd International Conference on Inventive Computing and Informatics (ICICI)

作者： Guo, Shuguang Jiangsu Maritime Inst Nanjing 211170 Jiangsu Peoples R China

ISBN: (纸本)9798350373301;9798350373295

This study explores the application of multi-objective optimization algorithms in logistics sensor networks, with a particular focus on real-time tracking in cold chain logistics. Leveraging technologies such as RFID, sensor networks, and intelligent computing, the study proposes efficient solutions for inventory management, route optimization, and smart logistics. The proposed HEMO-MRSS algorithm addresses the Vehicle Routing Problem (VRP) to minimize the number of vehicles and increase efficiency. Through a comprehensive literature review, recent advances in wireless sensor networks (WSNs) are examined and their contributions to various domains are highlighted. The methodology section describes the design of logistics systems with sensors and the application of multi-objective optimization algorithms, and presents a detailed model for integrated vehicle routing with time windows. The simulation, performed on a Windows 11 system, evaluates the effectiveness of the proposed algorithms using four evaluation metrics. The results show improved performance under optimized conditions, especially in the mean and optimal values, suggesting the importance of optimization in improving algorithmic efficiency in logistics sensor networks.

关键词： Multi-Objective optimization Logistics Sensor Networks Intelligent Management optimization algorithms

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An Exploration of optimization algorithms for High Performance Tensor Completion 16

An Exploration of Optimization Algorithms for High Performan...

引用

International Conference on High Performance Computing, Networking, Storage and Analysis (SC)

作者： Smith, Shaden Park, Jongsoo Karypis, George Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA Intel Corp Parallel Comp Lab Santa Clara CA 95051 USA

ISBN: (纸本)9781467388153

Tensor completion is a powerful tool used to estimate or recover missing values in multi-way data. It has seen great success in domains such as product recommendation and healthcare. Tensor completion is most often accomplished via low-rank sparse tensor factorization, a computationally expensive non-convex optimization problem which has only recently been studied in the context of parallel computing. In this work, we study three optimization algorithms that have been successfully applied to tensor completion: alternating least squares (ALS), stochastic gradient descent (SGD), and coordinate descent (CCD++). We explore opportunities for parallelism on shared-and distributed-memory systems and address challenges such as memory-and operation-efficiency, load balance, cache locality, and communication. Among our advancements are an SGD algorithm which combines stratification with asynchronous communication, an ALS algorithm rich in level-3 BLAS routines, and a communication-efficient CCD++ algorithm. We evaluate our optimizations on a variety of real datasets using a modern supercomputer and demonstrate speedups through 1024 cores. These improvements effectively reduce time-to-solution from hours to seconds on real-world datasets. We show that after our optimizations, ALS is advantageous on parallel systems of small-to-moderate scale, while both ALS and CCD++ will provide the lowest time-to-solution on large-scale distributed systems.

关键词： Tensile stress optimization Parallel processing Distributed databases Mathematical model Complexity theory Prediction algorithms optimization algorithms PARALLEL PROCESSING (COMPUTERS) distributed databases Prediction algorithms Complexity theory Tensile stress Mathematical Model Tensor Amyotrophic Lateral Sclerosis ADVANCED LIGHT SOURCE ambient light sensor Charge coupled devices Completion Cache Memory System parallel system

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