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检索条件"主题词=Optimization algorithms"

共 3923 条记录，以下是121-130 订阅

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Using of Evolutionary optimization algorithms in Hybrid Renewable Energy Systems

Using of Evolutionary Optimization Algorithms in Hybrid Rene...

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作者： Alshammari, Nahar Western Michigan University

学位级别：Ph.D., Doctor of Philosophy

While hybrid renewable energy systems (HRES) increase reliability, they are the technologies used to optimize to ensure cost-effectiveness and technical feasibility. Hence, a comprehensive mathematical model for the basic version of the Hybrid Renewable Dilemma has been developed to implement heuristic algorithms for solving major problems. The model's precision lets the system be used for broader issues that are more complicated. The Harmony Search (HS), Jaya, and Particle Swarm optimization (PSO) algorithms in this dissertation are used to optimize the design of a hybrid off-grid renewable energy system comprising a collection of wind/photovoltaic/biomass/battery systems to provide the necessary electrical demand for a remote region in Kingdom of Saudi Arabia using heuristic approaches in a cost-effective, productive, and reliable way. The MATLAB software has been used in all simulations to converge to an optimal solution for the sizing problem. A primary assessment of the proposed system was made using the HOMER program to assess the boundaries of the numbers of the system components that would be included in the proposed system. The outcomes of the three algorithms are compared, and the one with the most technoeconomic optimum unit sizing of the HRES was determined. The hybrid system’s reliability and efficiency are calculated using two factors: the maximum allowable loss of power supply probability (LPSPmax) and minimum allowable excess energy fraction (EFFmin). A statistical procedure based on the use of an independent t-test sample to be used in the evaluation of different optimization algorithms was presented in this dissertation to assist in determining the best algorithm. To resolve the optimum configuration of a hybrid renewable energy system, this procedure was applied to five heuristic meta-detection algorithms, namely particle swarm optimization (PSO), Harmony Search (HS), Firefly algorithm (FA), Cultural Algorithm (CA), and Flower Pollination Algorith

关键词： Hybrid renewable energy systems optimization algorithms Particle Swarm optimization

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Welding diagnostics based on feature selection and optimization algorithms

Welding diagnostics based on feature selection and optimizat...

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Conference on Optical Sensing and Detection

作者： Mirapeix, J. Cobo, A. Garcia-Allende, P. B. Conde, O. M. Lopez-Higuera, J. M. Univ Cantabria Photon Engn Grp E-39005 Santander Spain

ISBN: (纸本)9780819481993

In a previous paper a new approach was explored where the output parameters of a welding monitoring system based on plasma spectroscopy were the participation profiles of plasma ions and neutral atoms. They were obtained by the generation of synthetic spectra and the use of an optimization algorithm, showing correlation to the appearance of defects on the seams. In this work a feature selection algorithm is included in the model to determine the most discriminant wavelengths in terms of defect detection, thus allowing to reduce the spectral range where the synthetic spectra are generated. This should also give rise to an improvement in the overall computational performance of the algorithm. Alternatives to the use of controlled randomn search algorithms will be also explored, and the resulting model will be checked by means of experimental and field tests of arc-welding processes.

关键词： arc-welding on-line monitoring plasma optical spectroscopy optimization algorithms particle swarm feature selection

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Application of optimization algorithms to Structural Engineering Problems

Application of Optimization Algorithms to Structural Enginee...

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作者： Kashani, Alireza The University of Memphis

学位级别：Ph.D., Doctor of Philosophy

The main application of optimization algorithms to engineering problems is to facilitate the problem-solving procedure by finding good solutions. In this way, there are two main categories of problems that are tackled by the optimization algorithms: analyzing and designing. The key part of an optimization project, in addition to the type of algorithms, is objective functions. Therefore, a good understanding of the nature of the problems at hand would be crucial for defining the optimization procedure. To be more precise, it is important how to define the boundaries of the input parameters, the applied constraints and estimate the objective values. This dissertation is aimed to implement recent optimization algorithms for finding cost-effective designs for different structures. The underlying goal is to automate the cost-effective design procedure, find efficient optimizers, explore the interactions of conflicting objectives through multi-objective optimization and see their effects on the final results. In this way, different MATLAB codes are developed to automate the design procedures of reinforced concrete cantilever retaining walls (RCC wall), mechanically stabilized earth retaining walls (MSE wall), shallow foundations, and combined footing. Moreover, the performance of a wide range of swarm-intelligence-based and evolutionary algorithms are explored in solving the mentioned problems. A self-adaptive hybrid evolutionary optimization algorithm is developed based on combining genetic algorithm (GA) and particle swarm optimization (PSO) called GAPSO to enhance the performance of recent variants of GA and PSO as much as possible. This algorithm is applied to the problem of RCC wall optimization. Cost minimization and factor of safety (FOS) maximization as two conflicting objectives for retaining structures optimization problems are studied in a multi-objective optimization process. In this way, FOSs of the wall against overturning, sliding, and bearing capacity are

关键词： optimization algorithms Structural engineering problems Analyzing and designing

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Possibilities and Problems of Station-Level optimization algorithms Integration into the Application Software of Program and Technical Complexes of Intelligent Automated Control Systems of TPP 20th

Possibilities and Problems of Station-Level Optimization Alg...

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20th IFAC Conference on Technology, Culture, and International Stability (TECIS)

作者： Arakelyan, E. K. Mezin, S., V Pashchenko, F. F. Kosoy, A. A. Natl Res Univ MPEI ACSHP Dept Moscow 111250 Russia Russian Acad Sci VA Trapeznikov Inst Control Sci Moscow 117997 Russia

The possibility of integrating station -level optimization algorithms into the application software of modern program and technical complexes (PTC) in order to increase the intelligence of the automatic control systems (ACS TP) of thermal power plants is considered. On the example of the actual task of managing the current technical and economic indicators (TEl) of power units, general methodological approaches to the development of a set of algorithms for calculating and analyzing current and regulatory indicators are considered. The article presents a methodology for evaluating the feasibility and timing of restoration work on the cooling surfaces of the condenser in order to eliminate or minimize the deviation of the current increased pressure in it from the standard value. The complexities of integrating algorithms for calculating and analyzing block -level TEI into the PTC software are shown, and possible ways to solve them are given. Copyright (C) 2021 The Authors.

关键词： Integration optimization algorithms application software program and technical complexes TEI calculation and analysis general approaches station level possibility complexity solutions

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Accelerated optimization algorithms for statistical 3D x-ray computed tomography image reconstruction

Accelerated optimization algorithms for statistical 3D x-ray...

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作者： Kim, Donghwan University of Michigan

学位级别：Ph.D.

X-ray computed tomography (CT) has been widely celebrated for its ability to visualize the anatomical information of patients, but has been criticized for high radiation exposure. Statistical image reconstruction algorithms in X-ray CT can provide improved image quality for reduced dose levels in contrast to the conventional reconstruction methods like filtered back-projection (FBP). However, the statistical approach requires substantial computation time, more than half an hour for commercial 3D X-ray CT products. Therefore, this dissertation focuses on developing iterative algorithms for statistical reconstruction that converge within fewer iterations and that are amenable to massive parallelization in modern multi-processor implementations. Ordered subsets (OS) methods have been used widely in tomography problems, because they reduce the computational cost by using only a subset of the measurement data per iteration. This dissertation first improves OS methods so that they better handle 3D helical cone-beam CT geometries. OS methods have been used in commercial positron emission tomography (PET) and single-photon emission CT (SPECT) since 1997. However, they require too long a reconstruction time in X-ray CT to be used routinely for every clinical CT scan. In this dissertation, two main approaches are proposed for accelerating OS algorithms, one that uses new optimization transfer approaches and one that combines OS with momentum algorithms. First, the separable quadratic surrogates (SQS) methods, a widely used optimization transfer method with OS methods yielding simple, efficient and massively parallelizable OS-SQS methods, have been accelerated in three different ways; a nonuniform SQS (NU-SQS), a SQS with bounded interval (SQS-BI), and a quasi-separable quadratic surrogates (QSQS) method. Among them, a new NU-SQS method that encourages larger step sizes for the voxels that are expected to change more between the current and the final image has highly accelerat

关键词： Computed Tomography Statistical Image Reconstruction optimization algorithms Iterative algorithms Ordered Subsets Gradient Methods Thesis

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Indoor UWB Localization Based on Improved Intelligent optimization algorithms 6

Indoor UWB Localization Based on Improved Intelligent Optimi...

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6th International Conference on Internet of Things, Automation and Artificial Intelligence, IoTAAI 2024

作者： Li, Shuaichen Wu, Jianfeng Chinese Academy of Sciences National Time Service Center Xian710600 China Chinese Academy of Sciences Key Laboratory of Time Reference and Applications Xi'an710600 China University of Chinese Academy of Sciences School of Integrated Circuits Beijing100049 China University of Chinese Academy of Sciences College of Electronic Electrical and Communication Engineering Beijing100049 China

ISBN: (纸本)9798350386974

In response to the slow convergence of the original intelligent optimization algorithm and the possibility of converging to locally optimal solutions, this paper proposes the Logistic Mapping-Gaussian Mutation-Spiral Flight improvement strategy. The improvement in performance by the proposed strategy for the intelligent optimization algorithm is verified using the Grey Wolf optimization Algorithm (GWO) and Egret Swarm optimization Algorithm (ESOA). First, the improved GWO algorithm was tested with both unimodal and multimodal standard test functions, and the results were compared. The results show that the improved algorithm has a significant improvement in the accuracy and stability. Next, the improvement strategy was applied to the ESOA algorithm to solve for indoor positioning in UWB. The experimental results show that the improved algorithm significantly reduces both positioning error and standard deviation, which was reduced by 17.69% compared with the algorithm before the improvement, and the standard deviation is approximately 1/10 of the pre improvement level. Moreover, Improved algorithm requires a smaller minimum population and fewer iterations and has better convergence ability. These results indicate that the proposed improvement strategy can effectively enhance the performance of intelligent optimization algorithms and provide new ideas and methods for better solving optimization problems and indoor UWB positioning problems. © 2024 IEEE.

关键词： optimization algorithms

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A Comparative Study on Nature Inspired optimization algorithms to Solve Real World Problems- A Survey 5

A Comparative Study on Nature Inspired Optimization Algorith...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Bharathi, A. Ponnila, P. Daniel Madan Raja, S. Karthiga, M. Kcg College of Technology Computer Science & Engineering Chennai India Bannari Amman Institute of Technology Information Technology Erode Sathyamangalam India Karunya Institute of Technology and Sciences School of Computer Science and Engineering Coimbatore India Bannari Amman Institute of Technology Computer Science and Engineering Erode Sathyamangalam India

ISBN: (纸本)9798331504403

optimization plays a vital role in science and engineering. Nonlinear problems demand optimized solutions, traditional algorithms fail to solvethese kinds of problems. A Recent trend is to use Nature Inspired optimization algorithms due to their adaptability and effectiveness. Nature itself gives many optimized solutions for solving real world problems. The main objective of using optimization algorithms is to minimize the cost, energy consumption and maximize the effectiveness, maintenance and execution. This paper reviews the various nature Inspired optimization algorithms used to solve optimization problems. © 2024 IEEE.

关键词： optimization algorithms

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Evaluation of Optimal Weibull Parameter for Wind Resource Assessment at Coastal Terrain by Metaheuristic optimization algorithms Using Reanalysis Data 1

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9th International Conference on Advances in Energy Research, ICAER 2023

作者： Shende, Vikas Patidar, Harsh Baredar, Prashant Soni, Archana Bhopal462003 India

ISBN: (数字)9789819754199

ISBN: (纸本)9789819754182

This study focuses on evaluating wind resources at a coastal terrain in India by optimizing the parameters of the Weibull distribution. The research utilizes a 22-year dataset from NASA MERRA-2 reanalysis to analyze wind characteristics. The Weibull probability distribution is used, and the shape (k) and scale (A) parameters are determined through three different numerical techniques [modified maximum likelihood method (MMLM), empirical method of Lysen (EML), and method of moment (MOM)] along with two metaheuristic optimization algorithms [particle swarm optimization (PSO) and genetic algorithm (GA)] at a height of 50 m. The accuracy of the methods is assessed using goodness of fit tests, RMSE, and R2. The results indicate that all parameter estimation methods are suitable, but the MMLM is the most accurate for evaluating wind potential. As compared to GA, PSO was shown to be more efficient. These findings are anticipated to contribute to existing knowledge on probabilistic modeling of wind resources and the usefulness of MERRA-2 in assessing wind resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optimization algorithms

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Advancing Automated Machine Learning: Neural Architectures and optimization algorithms

Advancing Automated Machine Learning: Neural Architectures a...

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作者： Chen, Xiangning University of California Los Angeles

学位级别：Ph.D., Doctor of Philosophy

The field of Automated Machine Learning (AutoML) has gained immense attention for its ability to automate complex machine learning tasks, yet it is still an evolving discipline requiring nuanced approaches to be fully realized. This thesis, "Advancing Automated Machine Learning: Neural Network Architectures and optimization algorithms," provides a comprehensive investigation into two foundational pillars: Neural Architecture Search (NAS) and optimization *** the first half of the thesis, we confront the inherent challenges of stability and robustness in NAS, enhancing its reliability through a perturbation-based regularization scheme. This allows for more consistent and dependable architecture choices. Furthermore, we extend the traditional paradigms of NAS by framing it as a distribution learning problem, and additionally, by applying it to collaborative filtering. These extensions not only broaden the applicability of NAS but also lead to marked improvements in the efficiency and accuracy of recommendation *** latter part of the thesis focuses on the role of optimization in achieving high performance, particularly in transformer architectures. We identify a critical optimization gap and propose strategies for its mitigation, emphasizing the necessity of a transition from purely architecture-based search to include optimization techniques. Then we delve into a groundbreaking approach to optimization algorithm design through symbolic program discovery. This framework automatically discover new optimization methods that outperform traditional algorithms, thereby introducing an unprecedented level of automation in the development of optimization techniques. Our developed Lion algorithm has been widely adopted by the community. This not only advances the state-of-the-art in optimization algorithms but also significantly augments the capabilities and reach of AutoML *** addressing these multifaceted challenges in both neural architecture and opt

关键词： Automated Machine Learning Neural Architecture Search Neural Network Architectures optimization algorithms optimization techniques

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Implementation of Non-Smooth optimization algorithms for Logistics Problems 3

Implementation of Non-Smooth Optimization Algorithms for Log...

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3rd International Conference on Problems of Logistics, Management and Operation in the East-West Transport Corridor, PLMO 2024

作者： Zhurbenko, Mykola V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine Department of non-smooth optimization methods Kyiv Ukraine

ISBN: (纸本)9798350353099

The paper presents experience in using non-smooth optimization algorithms to solve problems of long-term planning of the functioning and development of transport systems. The class of problems under consideration is formulated as mathematical programming problems. Mathematical models are characterized by high dimensionality and block structure of constraints. The solution method is based on the use of effective non-smooth optimization algorithms in combination with decomposition methods. As a rule, solving a problem consists of two stages. At the first stage, a dual problem is solved regarding general constraints that contain variables from various blocks of constraints. At the second stage, the solution to the initial problem is determined. Numerical aspects of this approach to solving applied logistics problems are discussed. A brief description of recently developed subgradient algorithms is given. © 2024 IEEE.

关键词： optimization algorithms

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