检索结果-内蒙古大学图书馆

A Fast Distributed Asynchronous Newton-Based optimization Algorithm

IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2020年第7期65卷 2769-2784页

作者： Mansoori, Fatemeh Wei, Ermin Northwestern Univ Dept Elect & Comp Engn Evanston IL 60208 USA

One of the most important problems in the field of distributed optimization is the problem of minimizing a sum of local convex objective functions over a networked system. Most of the existing work in this area focuses on developing distributed algorithms in a synchronous setting under the presence of a central clock, where the agents need to wait for the slowest one to finish the update, before proceeding to the next iterate. Asynchronous distributed algorithms remove the need for a central coordinator, reduce the synchronization wait, and allow some agents to compute faster and execute more iterations. In the asynchronous setting, the only known algorithms for solving this problem could achieve an either linear or sublinear rate of convergence. In this paper, we build upon the existing literature to develop and analyze an asynchronous Newton-based method to solve a penalized version of the problem. We show that this algorithm guarantees almost sure convergence with a global linear and local quadratic rate in expectation. Numerical studies confirm the superior performance of our algorithm against other asynchronous methods.

关键词： optimization Convergence Linear programming Approximation algorithms Delays Clocks Symmetric matrices Agents and autonomous systems asynchronous algorithms network analysis and control optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Complexity Certification of Proximal-Point Methods for Numerically Stable Quadratic Programming

引用

IEEE CONTROL SYSTEMS LETTERS 2021年第4期5卷 1381-1386页

作者： Arnstrom, Daniel Bemporad, Alberto Axehill, Daniel Linkoping Univ Div Automat Control SE-58183 Linkoping Sweden IMT Sch Adv Studies Lucca Dept Comp Sci & Engn I-55100 Lucca Italy

When solving a quadratic program (QP), one can improve the numerical stability of any QP solver by performing proximal-point outer iterations, resulting in solving a sequence of better conditioned QPs. In this letter we present a method which, for a given multi-parametric quadratic program (mpQP) and any polyhedral set of parameters, determines which sequences of QPs will have to be solved when using outer proximal-point iterations. By knowing this sequence, bounds on the worst-case complexity of the method can be obtained, which is of importance in, for example, real-time model predictive control (MPC) applications. Moreover, we combine the proposed method with previous work on complexity certification for active-set methods to obtain a more detailed certification of the proximal-point method's complexity, namely the total number of inner iterations.

关键词： optimization algorithms predictive control for linear systems

来源：评论

学校读者我要写书评

暂无评论

Stochastic Safety for Markov Chains

引用

IEEE CONTROL SYSTEMS LETTERS 2021年第2期5卷 427-432页

作者： Bujorianu, Manuela L. Wisniewski, Rafael Boulougouris, Evangelos Univ Strathclyde Maritime Safety Res Ctr Dept Naval Architecture Ocean & Marine Engn Glasgow G4 0LZ Lanark Scotland Aalborg Univ Dept Elect Syst Automat & Control DK-9220 Aalborg Denmark

In this letter, we study the so-called p-safety of a Markov chain. We say that a state is p-safe in a state space S with respect to an unsafe set U if the process stays in the state space and hits the set U with the probability less than p. We show several ways of computing p-safety: by means the Dirichlet problem, the evolution equation, the barrier certificates, and the Martin kernel. The set of barrier certificates forms a cone. We show how to generate barrier certificates from the set of extreme points of a cone base.

关键词： Stochastic systems optimization algorithms Markov processes Lyapunov methods numerical algorithms computational methods

来源：评论

学校读者我要写书评

暂无评论

Fully Distributed Nash Equilibrium Seeking Over Time-Varying Communication Networks With Linear Convergence Rate

引用

IEEE CONTROL SYSTEMS LETTERS 2021年第2期5卷 499-504页

作者： Bianchi, Mattia Grammatico, Sergio Delft Univ Technol Delft Ctr Syst & Control NL-2613 GA Delft Netherlands

We design a distributed algorithm for learning Nash equilibria over time-varying communication networks in a partial-decision information scenario, where each agent can access its own cost function and local feasible set, but can only observe the actions of some neighbors. Our algorithm is based on projected pseudo-gradient dynamics, augmented with consensual terms. Under strong monotonicity and Lipschitz continuity of the game mapping, we provide a simple proof of linear convergence, based on a contractivity property of the iterates. Compared to similar solutions proposed in literature, we also allow for time-varying communication and derive tighter bounds on the step sizes that ensure convergence. In fact, in our numerical simulations, our algorithm outperforms the existing gradient-based methods, when the step sizes are set to their theoretical upper bounds. Finally, to relax the assumptions on the network structure, we propose a different pseudo-gradient algorithm, which is guaranteed to converge on time-varying balanced directed graphs.

关键词： Games Heuristic algorithms Convergence Communication networks Directed graphs Nash equilibrium Upper bound Game theory optimization algorithms networked control systems

来源：评论

学校读者我要写书评

暂无评论

Optimal Linear Crossover for Mitigating Negative Transfer in Evolutionary Multitasking

引用

IEEE Transactions on Evolutionary Computation 2024年

作者： Liu, Zhaobo Yuan, Jianhua Zhang, Haili Zeng, Tao Zhu, Zexuan Shenzhen University Institute for Advanced Study Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shenzhen Polytechnic University School of Undergraduate Education Shenzhen518055 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University Shenzhen City Key Laboratory of Embedded System Design Shenzhen518060 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education of China Chongqing400044 China

Evolutionary multitasking algorithms use information exchange among individuals in a population to solve multiple optimization problems simultaneously. Negative transfer is a critical factor that affects the performance of evolutionary multitasking algorithms. In this study, we propose an innovative approach to mitigate negative transfer in evolutionary multitasking algorithms. The proposed approach is grounded in rigorous theoretical analysis, which provides valuable theoretical insights into the design of an optimal linear crossover operator for mitigating negative transfer. By identifying interpretable conditions, we establish a solid theoretical foundation to prevent negative transfer in diverse scenarios. Building upon these findings, we theoretically derive a closed-form expression for the optimal crossover operator and propose practical design methods based on approximations. Furthermore, we integrate the proposed optimal crossover operator into a fundamental evolutionary multitasking algorithm framework. The resultant algorithm is comparable or superior to other state-of-the-art methods. Empirical validation through comprehensive experiments confirms the effectiveness of our theoretical findings. © 1997-2012 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Impacts of changes in the watershed partitioning level and optimization algorithm on runoff simulation: decomposition of uncertainties

引用

STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2020年第11期34卷 1909-1923页

作者： Zhou, Shuai Wang, Yimin Guo, Aijun Li, Ziyan Chang, Jianxia Meng, Dongdong Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Peoples R China

Hydrological modeling has provided key insights into the mechanisms of model state, such as the watershed partitioning level and optimization algorithm, and their impacts on the hydrological process, but the uncertainty of this impact is poorly understood. To this end, in this study, the effects of the watershed partitioning level and optimization algorithm for hydrological simulation uncertainty were assessed based on the semi-distributed TOPMODEL model, i.e., six watershed partitioning levels and three intelligent global optimization algorithms were used in the source region of the Yellow River. Meanwhile, the uncertainty contribution of the individual and interaction of the watershed partitioning levels and optimization algorithms on the hydrological process were dynamically evaluated using the variance decomposition method based on subsampling. Results showed that the impacts of the watershed partitioning level and optimization algorithm on the runoff simulation were particularly obvious for different characteristic periods. In the flood period, the optimization algorithm was the dominant factor affecting the runoff simulation uncertainty, with the proportion of up to 0.50, whereas the contribution of the watershed partitioning level was only 0.22. In the non-flood period, they contributed substantially to the uncertainty of the runoff simulation, accounting for about 0.30. Moreover, the interactions between the watershed partitioning level and optimization algorithm had a strong influence throughout the year, especially in the non-flood period, which may be because the hydrological model amplifies the output error and increases the interaction effect. Generally, the results shed important insight into reducing the uncertainty of the runoff simulation in future research.

关键词： Interaction optimization algorithms Watershed partitioning level TOPMODEL Yellow River

来源：评论

学校读者我要写书评

暂无评论

College English Teaching Quality Evaluation Method Based on Differential Evolution Algorithm

引用

International Journal of High Speed Electronics and Systems 2024年第0期

作者： Zhang, Suojun Jiao, Haiqi School of Foreign Languages Quzhou University Quzhou324000 China China Mobile Group Inner Mongolia Co. Ltd. Hulunbuir Branch Hulunbuir021008 China

This study proposes a college English teaching quality evaluation method based on the differential evolution (DE) algorithm. Traditional evaluation methods are often subjective, lacking objectivity and relying on qualitative analysis, which can be insufficient for comprehensive and systematic assessments. In this context, the DE algorithm is integrated into the evaluation system to adaptively adjust the weights of evaluation indicators, thereby better reflecting the key factors of teaching quality. The efficacy of the proposed method is demonstrated through experiments conducted on real-world college English courses. The results suggest that, compared to traditional methods, the DE-based evaluation is more objective, comprehensive, and precise, offering tailored recommendations for improvement. This novel evaluation method provides a fresh perspective for enhancing teaching quality and offers a robust decision-making foundation for administrators, instructors, and policymakers. The key contribution of this study lies in the innovative integration of the DE algorithm into teaching evaluation, addressing challenges of objectivity and comprehensiveness that traditional methods often face. The proposed approach enhances evaluation objectivity and provides a systematic tool for educational reform and teaching optimization. The findings of this research are expected to contribute to the ongoing enhancement of the college English teaching quality evaluation system. © 2025 The Author(s).

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A Newton Method for Hausdorff Approximations of the Pareto Front Within Multi-objective Evolutionary algorithms

引用

IEEE Transactions on Evolutionary Computation 2024年

作者： Wang, Hao Rodriguez-Fernandez, Angel E. Uribe, Lourdes Deutz, Andre Cortes-Pina, Oziel Schutze, Oliver Leiden Institute of Advanced Computer Science of Leiden University Leiden Netherlands Applied Quantum Algorithms Group Leiden University Netherlands Computer Science Department of the CINVESTAV Mexico City Mexico Faculty of Actuarial Sciences of Universidad Anáhuac México Mexico IPN Mexico City Mexico

A common goal in evolutionary multi-objective optimization is to find suitable finite-size approximations of the Pareto front of a given multi-objective optimization problem. While many multi-objective evolutionary algorithms have proven to be very efficient in finding good Pareto front approximations, they may need quite a few resources or may even fail to obtain optimal or nearly optimal approximations. Hereby, optimality is implicitly defined by the chosen performance indicator In this work, we propose a set-based Newton method for Hausdorff approximations of the Pareto front to be used within multi-objective evolutionary algorithms. To this end, we first generalize the previously proposed Newton step for the performance indicator to treat constrained problems for general reference sets. To approximate the target Pareto front, we propose a particular strategy for generating the reference set that utilizes the data gathered by the evolutionary algorithm during its run. Finally, we show the benefit of the Newton method as a post-processing step on several benchmark test functions and different base evolutionary algorithms. © 1997-2012 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

A Survey of Multi-objective Evolutionary Algorithm Based on Decomposition: Past and Future

引用

IEEE Transactions on Evolutionary Computation 2024年

作者： Li, Ke University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Exeter Department of Computer Science ExeterEX4 4RN United Kingdom

Decomposition has been the mainstream approach in the classic mathematical programming for multi-objective optimization and multi-criterion decision-making. However, it was not properly studied in the context of evolutionary multi-objective optimization until the development of multi-objective evolutionary algorithm based on decomposition (MOEA/D). In this article, we present a comprehensive survey of the development of MOEA/D from its origin to the current state-of-the-art. In order to be self-contained, we start with a step-by-step tutorial that aims to help a novice quickly get onto the working mechanism of MOEA/D. Then, selected major developments of MOEA/D are reviewed according to its core design components including subproblem formulations, selection mechanisms and reproduction operators. Besides, we also overviews some further developments for constraint handling, large-scale problems, computationally expensive objective functions, preference incorporation, and real-world applications. In the final part, we shed some lights on emerging directions for future developments. © 1997-2012 IEEE.

关键词： optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Leveraging Multiple Connected Traffic Light Signals in an Energy-Efficient Speed Planner

引用

IEEE CONTROL SYSTEMS LETTERS 2021年第6期5卷 2078-2083页

作者： Han, Jihun Shen, Daliang Karbowski, Dominik Rousseau, Aymeric Argonne Natl Lab Energy Syst Div Lemont IL 60439 USA

Connecting automated vehicles to traffic lights can lead to significant energy savings by enabling them to pass through intersections in an energy-efficient way without unnecessary stops. A cellular-based communication system connecting multiple traffic lights can help realize the full potential of energy-efficient driving at intersections. Thus, we propose a hierarchical speed planner that can leverage information from multiple connected traffic lights. The proposed speed planner consists of two modules: a green window selector and a reference trajectory generator. The green window selector, based on Dijkstra's algorithm, finds a series of "green windows" for connected traffic lights that builds an energy-optimal path for vehicles to follow. The reference trajectory generator finds optimal entering times, based on the selected green window at each intersection, and then computes reference trajectories. Deriving and using analytical optimal entering speeds as a function of entering times allows us to guarantee the computational simplicity suitable for real-time implementation. We also demonstrate how to balance energy and traffic flow perspectives in the reference trajectory generator. Finally, a high-fidelity simulation framework is used to evaluate the proposed speed planner and quantify the extent to which it can save energy in various real-world urban route scenarios.

关键词： Automotive control autonomous vehicles optimal control optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：