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检索条件"主题词=Optimization algorithms"

共 4015 条记录，以下是581-590 订阅

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SUPPRESSION OF HIGH FREQUENCY OSCILLATIONS ON SUSPENSION SYSTEM OF SNOW GROOMER WITH EVOLUTIONARY algorithms optimization 9

SUPPRESSION OF HIGH FREQUENCY OSCILLATIONS ON SUSPENSION SYS...

引用

9th FPNI Ph D Symposium on Fluid Power (FPNI2016)

作者： Marani, Pietro Ferrari, Cristian IMAMOTER CNR Ferrara Italy

ISBN: (纸本)9780791850473

In current engineering practice for the design and dimensioning of hydropneumatic suspension systems, the effect of main parameters is considered;this approach can be used to implement approximate models basically suitable to describe low frequency and high amplitude oscillations of the machine. The target of this study is a Snow Groomer, a tracked vehicle driven by diesel engines and equipped in front with a shovel and behind with a cutter. When the machine drives over a snowfield, it pushes snow ahead of it and, at the same time, smooths out any surface unevenness. The suspension system is the key element to ensure the driver's safety and comfort, the effectiveness of snow grooming and finally enhance the reliability of the machine components. The on-field testing had shown high frequency pressure oscillations transmitted from the sprocket to hydraulic system, propagated through the flexible hoses. Those Pressure Oscillations cause noise and can affect negatively the durability and reliability of the Machine. A lumped parameter non-linear dynamic model of the hydraulic circuit and of the machine interactions is built in Amesim environment, including Lax Wendroff wave propagation models, to make it able to catch the high frequency oscillations experienced in the test field. Most of the design parameters are fixed (such as vehicle weight and hydraulic lines length), other parameters can be varied to study the optimal solution, these parameters define the "factors" of the optimization problem. As a next step it is important to define the objectives of the optimization, in this case corresponding to various figures of merit describing the behavior of the system in different work conditions. The large number of factors included in the lumped parameter model generates an exponentially larger number of possible configurations. Moreover the relationship between factors and objective is not always possible to express with explicit mathematical models. Finally the presence

关键词： optimization Suspension systems Pressure Tracked vehicles Design Genetic algorithms Machine components Evolutionary algorithms Lumped parameter models Testing Hydraulic circuits algorithms Oscillations optimization algorithms Durability Dimensions Diesel engines Safety Noise (Sound) Vehicles Reliability Simulation Pareto optimization Particulate matter Dynamic models Machinery Wave propagation Weight (Mass) Hydraulic drive systems

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Efficient algorithms for Empirical Group Distributionally Robust optimization and Beyond 41

Efficient Algorithms for Empirical Group Distributionally Ro...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Yu, Dingzhi Cai, Yunuo Jiang, Wei Zhang, Lijun National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Data Science Fudan University Shanghai China Guangzhou China

In this paper, we investigate the empirical counterpart of Group Distributionally Robust optimization (GDRO), which aims to minimize the maximal empirical risk across m distinct groups. We formulate empirical GDRO as a two-level finite-sum convex-concave minimax optimization problem and develop an algorithm called ALEG to benefit from its special structure. ALEG is a double-looped stochastic primal-dual algorithm that incorporates variance reduction techniques into a modified mirror prox routine. To exploit the two-level finite-sum structure, we propose a simple group sampling strategy to construct the stochastic gradient with a smaller Lipschitz constant and then perform variance reduction for all groups. Theoretical analysis shows that ALEG achieves Ε-accuracy within a computation complexity of (equation presented), where n¯ is the average number of samples among m groups. Notably, our approach outperforms the state-of-the-art method by a factor of √m. Based on ALEG, we further develop a two-stage optimization algorithm called ALEM to deal with the empirical Minimax Excess Risk optimization (MERO) problem. The computation complexity of ALEM nearly matches that of ALEG, surpassing the rates of existing methods. Copyright 2024 by the author(s)

关键词： optimization algorithms

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Multi-objective optimization of Trusses Using Rao algorithms 7th

Multi-objective Optimization of Trusses Using Rao Algorithms

引用

7th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2024

作者： Pham, Hoang-Anh Dang, Viet-Hung Department of Structural Mechanics Ha Noi University of Civil Engineering 55 Giai Phong Hanoi Viet Nam Ha Noi University of Civil Engineering 55 Giai Phong Hanoi Viet Nam

ISBN: (纸本)9789819719716

The Rao algorithms are utilized to perform the multi-objective optimization of truss structures in this paper. Rao algorithms are new parameter-free meta-heuristics for global optimization. Due to their simplicity, these algorithms have been increasingly used to solve various engineering optimization problems. Nevertheless, the Rao algorithm’s applicability in multi-objective optimization is still limited. In the present study, the Rao algorithms, including Rao-1, Rao-2, are adapted to solve the multi-objective optimization for 2D and 3D trusses for the first time. The weight and the displacements of the trusses are considered optimization objectives, while the constraints are the member stresses. The effectiveness of the multi-objective Rao algorithms is examined by optimizing two numerical examples, and their performance is shown in comparison with some well-known algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： optimization algorithms

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Research on Seabed Slope Classification and Survey Path optimization Based on Genetic algorithms and Plane Fitting Method 2

Research on Seabed Slope Classification and Survey Path Opti...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Hong, Yonghang Cheng, Yexin Peng, Ziyu Business School Hohai University Nanjing China

ISBN: (纸本)9798350360240

Responding to the surge in maritime exploration and the increasing need for precise seabed mapping, this paper introduces a route planning method enhanced by an optimization algorithm to improve accuracy and efficiency. We develop three route planning models: north-south, east-west, and diagonal east-west directions. Utilizing a genetic algorithm for regional optimization and a planar fitting model to determine the shortest path in each region, our analysis reveals that the diagonal east-west direction yields the shortest route. The findings indicate improvements in route length, reduced missed areas, and lower overlap rates. This study offers a potent optimization algorithm for route planning, setting the stage for future advancements in the field. © 2024 IEEE.

关键词： optimization algorithms

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Test Suites and Performance of algorithms in Large-Scale Multiobjective Evolutionary optimization 13

Test Suites and Performance of Algorithms in Large-Scale Mul...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Gu, Haoran Wang, Handing Ma, Jingjing School of Artificial Intelligence Xidian University Xi'an China

ISBN: (纸本)9798350308365

In recent years, the research on large-scale mul-tiobjective optimization has attracted much attention. Many competitive large-scale multiobjective evolutionary algorithms have been proposed. Usually, their performance is evaluated on the widely used large-scale multiobjective test suite (i.e. LSMOP test suite). Those algorithms often exhibit a strong convergence capability on the instances of LSMOP test suite. The purpose of this study is to show our concern that the development of algorithms may be over specialized for the LSMOP test suite. We first explain some issues in the original LSMOP test suite. Then, we propose a general LSMOP test suite (termed GLSMOP), in which the Pareto set has a more general structure in the decision space. Experimental results on two test suites suggest that the performance of some large-scale multiobjective evolutionary algorithms will be deteriorated rapidly by the change of Pareto set. It also reveals the good performance of MOEAID-DE on large-scale multiobjective optimization problems. © 2024 IEEE.

关键词： optimization algorithms

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Barrier algorithms for Constrained Non-Convex optimization 41

Barrier Algorithms for Constrained Non-Convex Optimization

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41st International Conference on Machine Learning, ICML 2024

作者： Dvurechensky, Pavel Staudigl, Mathias Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany University of Mannheim Mannheim Germany

In this paper, we theoretically show that interior-point methods based on self-concordant barriers possess favorable global complexity beyond their standard application area of convex optimization. To do that we propose first- and second-order methods for non-convex optimization problems with general convex set constraints and linear constraints. Our methods attain a suitably defined class of approximate first- or second-order KKT points with the worst-case iteration complexity similar to unconstrained problems, namely O(Ε-2) (first-order) and O(Ε-3/2) (second-order), respectively. Copyright 2024 by the author(s)

关键词： optimization algorithms

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An Analysis of Microgrid Capacity optimization in Power Systems Based on Intelligent algorithms

An Analysis of Microgrid Capacity Optimization in Power Syst...

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2024 International Conference on Innovative Design and Intelligent Manufacturing, IDIM 2024

作者： Qu, Xinying Qu, Dongzhe Liu, Guangyu Yingkou Power Supply Company State Grid Liaoning Electric Power Co. Ltd. Yingkou115000 China

ISBN: (纸本)9781643685427

In order to solve the problem of unbalanced capacity ratio and overload of each micro power source in microgrid, an optimization analysis of microgrid capacity in power system based on intelligent algorithm is proposed. This paper aims at establishing a capacity optimization allocation model for grid-connected microgrids containing wind, solar, diesel and storage, taking the lowest comprehensive operating cost as the objective function, and taking the output of distributed power sources and pollutant emissions as constraints. The refractive reverse learning mechanism, differential mutation crossover selection strategy and dynamic step factor improved standard sparrow search algorithm are used to solve the model, and compared with the whale optimization algorithm, differential algorithm, gray wolf algorithm and sparrow search algorithm. Two typical days in a certain area were selected for example analysis. The experimental results show that the required costs are reduced by 3.05%, 4.12%, 8.46% and 1.13% respectively compared with the other four algorithms. Conclusion: The model proposed in this article is reasonable, and the improved SSA has good optimization capabilities. © 2024 The Authors.

关键词： optimization algorithms

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Contextually Adaptive algorithms for Gaussian Process Bandit optimization Under Heavy-Tailed Noise 27

Contextually Adaptive Algorithms for Gaussian Process Bandit...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Park, Hyeonjun Lee, Kyungjae The Department of Artificial Intelligence Chung-Ang University Seoul06974 Korea Republic of The Department of Statistics Korea University Seoul02841 Korea Republic of

ISBN: (纸本)9781643685489

We consider a Gaussian process (GP) bandit optimization problem when the objective function lives in a reproducing kernel Hilbert space (RKHS), assuming that the payoffs follow a heavy-tailed distribution with a bounded (1 + e)-th moment for some e ∈ (0, 1].Existing algorithms for this setting face practical challenges due to their significant computational demands and inconsistent theoretical guarantee to translation of noise *** address these issues, we introduce two robust *** first algorithm utilizes a truncation estimator, achieving the same regret bound as that of the existing algorithm up to logarithmic terms with reduced time *** second algorithm employs a median-of-means estimator and achieves more stable regret bound to alteration of noise distribution with lower time and space complexities compared to existing ***, we empirically validate the performance of our proposed algorithms against previous methods in both synthetic and real-world datasets. © 2024 The Authors.

关键词： optimization algorithms

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Implementing Genetic algorithms for Curriculum Scheduling and optimization in Higher Education

Implementing Genetic Algorithms for Curriculum Scheduling an...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： Capua, Roger D. Albina, A. Pio Shukla, Tushar Dhar Toledo, Sheena Marie Villegas Mayormente, Marvin Muthuperumal, S. Ifugao State University Philippines Alagappa University College of Education Alagappa University Department of Mathematics Tamilnadu Karaikudi India University Institute of Physical Education & Sports Chandigarh University Mohali India K. Ramakrishnan College of Engineering Department of Mathematics Trichy India

ISBN: (纸本)9798331539948

This research explores the application of Genetic algorithms (GA) as a novel approach to address the complex task of curriculum scheduling and optimization in higher education institutions. The conventional methods for creating and optimizing academic schedules often prove inefficient in handling the intricate constraints and preferences inherent in educational environments. The study begins by outlining the challenges associated with curriculum scheduling, including factors such as room availability, faculty constraints, student preferences, and course prerequisites. It then delves into the theoretical foundations of GA, illustrating how these algorithms can be adapted to model the optimization problem inherent in curriculum scheduling. The implementation phase involves the development of a customized GA tailored to the specific requirements of higher education scheduling. The algorithm is designed to evolve and improve schedules over successive generations, a variety of constraints and objectives. The research also explores the integration of user-defined parameters to accommodate institutional policies and stakeholder preferences. Results show that GAs greatly improve schedule adaptation and optimization, adding valuable insights to the growing research on evolutionary algorithms in education. The accuracy of the proposed model is 98. 52% which is 9.44% higher than existing methods. © 2024 IEEE.

关键词： optimization algorithms

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Design and analysis of intelligent heuristic search algorithms in mathematical optimization problems

Design and analysis of intelligent heuristic search algorith...

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2024 International Conference on algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Xia, Jing Shan, Jun School of Artificial Intelligence Wuhan Polytechnic University Wuhan430074 China

ISBN: (纸本)9781510685840

Heuristic algorithms have shown excellent solutions to combinatorial optimization problems. This paper delves into an improved heuristic algorithm and its practical application in solving combinatorial optimization related problems. It elaborates on the operation process of the algorithm and verifies its performance through carefully designed simulation experiments. Further in-depth analysis was conducted on the advantages and disadvantages of this algorithm to comprehensively understand its performance in practical problems. This improved heuristic algorithm not only has a solid theoretical foundation, but also demonstrates remarkable results in practical applications. © 2024 SPIE.

关键词： optimization algorithms

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