检索结果-内蒙古大学图书馆

Attack-Resilient distributed convex optimization of Cyber-Physical Systems Against Malicious Cyber-Attacks Over Random Digraphs

引用

IEEE INTERNET OF THINGS JOURNAL 2023年第1期10卷 458-472页

作者： Feng, Zhi Hu, Guoqiang Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

This article addresses a resilient exponential distributed convex optimization problem for a heterogeneous linear multiagent system under Denial-of-Service (DoS) attacks over random digraphs. The random digraphs are caused by unreliable networks and the DoS attacks, allowed to occur aperiodically, refer to an interruption of the communication channels carried out by the intelligent adversaries. In contrast to many existing distributed convex optimization works over a perfect communication network, the global optimal solution might not be sought under the adverse influences that result in performance degradations or even failures of optimization algorithms. The aforementioned setting poses certain technical challenges to optimization algorithm design and exponential convergence analysis. In this work, several resilient algorithms are presented such that a team of agents minimizes a sum of local nonquadratic cost functions in a safe and reliable manner with the global exponential convergence. Numerical simulation results are further presented to validate the effectiveness of the proposed distributed approaches.

关键词： Denial-of-Service (DoS) attack distributed convex optimization heterogeneous network linear multiagent system random digraph

来源：评论

学校读者我要写书评

暂无评论

Continuous-time distributed Heavy-ball Algorithm for distributed convex optimization over Undirected and Directed Graphs

引用

Machine Intelligence Research 2022年第1期19卷 75-88页

作者： Hao-Ran Yang Wei Ni School of Science Nanchang UniversityNanchang 330031China

This paper proposes second-order distributed algorithms over multi-agent networks to solve the convex optimization problem by utilizing the gradient tracking strategy, with convergence acceleration being achieved. Both the undirected and unbalanced directed graphs are considered, extending existing algorithms that primarily focus on undirected or balanced directed graphs. Our algorithms also have the advantage of abandoning the diminishing step-size strategy so that slow convergence can be avoided. Furthermore, the exact convergence to the optimal solution can be realized even under the constant step size adopted in this paper. Finally, two numerical examples are presented to show the convergence performance of our algorithms.

关键词： distributed convex optimization second-order distributed algorithm multi-agent systems gradient tracking directed graph

来源：评论

学校读者我要写书评

暂无评论

Online Learning Algorithm for distributed convex optimization With Time-Varying Coupled Constraints and Bandit Feedback

引用

IEEE TRANSACTIONS ON CYBERNETICS 2022年第2期52卷 1009-1020页

作者： Li, Jueyou Gu, Chuanye Wu, Zhiyou Huang, Tingwen Chongqing Normal Univ Sch Math Sci Chongqing 400047 Peoples R China Cutin Univ Dept Math & Stat Perth WA 6845 Australia Texas A&M Univ Qatar Dept Sci Doha Qatar

This article focuses on multiagent distributed-constrained optimization problems in a dynamic environment, in which a group of agents aims to cooperatively optimize a sum of time-changing local cost functions subject to time-varying coupled constraints. Both the local cost functions and constraint functions are unrevealed to an individual agent until an action is submitted. We first investigate a gradient-feedback scenario, where each agent can access both values and gradients of cost functions and constraint functions owned by itself at the chosen action. Then, we design a distributed primal-dual online learning algorithm and show that the proposed algorithm can achieve the sublinear bounds for both the regret and constraint violations. Furthermore, we extend the gradient-feedback algorithm to a gradient-free setup, where an individual agent has only attained the values of local cost functions and constraint functions at two queried points near the selected action. We develop a bandit version of the previous method and give the explicitly sublinear bounds on the expected regret and expected constraint violations. The results indicate that the bandit algorithm can achieve almost the same performance as the gradient-feedback algorithm under wild conditions. Finally, numerical simulations on an electric vehicle charging problem demonstrate the effectiveness of the proposed algorithms.

关键词： Cost function Linear programming convex functions Resource management Cybernetics Heuristic algorithms Bandit feedback distributed convex optimization online learning regret analysis time-varying constraint

来源：评论

学校读者我要写书评

暂无评论

A Multi-Scale Method for distributed convex optimization with Constraints

引用

JOURNAL OF optimization THEORY AND APPLICATIONS 2022年第1期192卷 379-400页

作者： Ni, Wei Wang, Xiaoli Nanchang Univ Sch Sci Nanchang 330031 Jiangxi Peoples R China Harbin Inst Technol Weihai Sch Informat Sci & Engn Weihai 264209 Peoples R China

This paper proposes a multi-scale method to design a continuous-time distributed algorithm for constrained convex optimization problems by using multi-agents with Markov switched network dynamics and noisy inter-agent communications. Unlike most previous work which mainly puts emphasis on dealing with fixed network topology, this paper tackles the challenging problem of investigating the joint effects of stochastic networks and the inter-agent communication noises on the distributed optimization dynamics, which has not been systemically studied in the past literature. Also, in sharp contrast to previous work in constrained optimization, we depart from the use of projected gradient flow which is non-smooth and hard to analyze;instead, we design a smooth optimization dynamics which leads to easier convergence analysis and more efficient numerical simulations. Moreover, the multi-scale method presented in this paper generalizes previously known distributed convex optimization algorithms from the fixed network topology to the switching case and the stochastic averaging obtained in this paper is a generalization of the existing deterministic averaging.

关键词： distributed convex optimization Multi-scale method Multi-agent systems Stochastic averaging Backward Kolmogorov equation

来源：评论

学校读者我要写书评

暂无评论

Multi-agent flocking control with complex obstacles and adaptive distributed convex optimization

引用

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2024年第16期361卷

作者： Pei, Huiqin Hu, Xinyu Luo, Zilong East China Jiaotong Univ Sch Elect & Automat Engn Nanchang 330013 Peoples R China

The optimization issue involving flocking control and obstacle avoidance behavior in continuous- time multi-agent systems is the main topic of this work. When an agent encounters an obstacle, a virtual agent is introduced to generate repulsive force, enabling the multi-agent system to navigate around obstacles smoothly. Under the local interaction between agents, a multi-agent flocking control and obstacle avoidance algorithm is proposed with utilizing adaptive distributed convex optimization. This algorithm can minimize the sum of local costs of the system. A symbolic function-based estimation method is introduced to effectively relax the constraints of the cost function. The Lyapunov approach is employed to show the stability of the multi-agent system. With the proposed algorithm, each agent can achieve flocking behavior for convex optimization in complex obstacle environments by interacting with their neighbors, virtual agents, and virtual leaders. Last but not least, simulation examples are given to further illustrate the effectiveness and efficiency of the suggested control algorithm.

关键词： Multi-agent distributed convex optimization Flocking control Obstacle avoidance

来源：评论

学校读者我要写书评

暂无评论

On f-Consensus Problems Solvable via distributed convex optimization Methods

On f-Consensus Problems Solvable via Distributed Convex Opti...

引用

第43届中国控制会议

作者： Siliang Yu Chao Huang College of Electronic and Information Engineering

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper is concerned with the distributed f-consensus problem in a multi-agent system, the aim of which is to achieve consensus on a particular value relating to function, f, of the labels owned by the agents. The paper utilizes distributed convex optimization as a tool to solve a series of f-consensus problems including network size estimation, k-th smallest value consensus and leader-following consensus(information sharing). The effectiveness of the theoretical results is verified by means of numerical simulations.

关键词： Multi-agent Systems Consensus distributed convex optimization

来源：评论

学校读者我要写书评

暂无评论

A distributed convex optimization Algorithm over Directed Networks with Time Delay

A Distributed Convex Optimization Algorithm over Directed Ne...

引用

第43届中国控制会议

作者： Yutian Wei Jin Xie Weifeng Gao Hong Li School of Mathematics and Statistics Xidian University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This paper aims to explore a novel distributed convex optimization algorithm over directed networks with communication delay based on zero-gradient-sum strategy to solve a class of the distributed convex optimization ***,distributed convex optimization problem is formulated as minimizing a separable global objective ***,the convergence of improved algorithm is analyzed by constructing a Lyapunov-Krasovskii *** addition,a time delay upper bound is derived to make sure that all agents cooperatively converge to the global optimal consistency as a sufficient ***,a numerical simulation example is given to verify the effectiveness of improved algorithm.

关键词： distributed convex optimization Zero-Gradient-Sum Time Delay Multi-agent System Directed Topology

来源：评论

学校读者我要写书评

暂无评论

Projected subgradient based distributed convex optimization with transmission noises

引用

APPLIED MATHEMATICS AND COMPUTATION 2022年 418卷 126794-126794页

作者： Zhang, Li Liu, Shuai Shandong Univ Sch Control Sci & Engn Jinan Peoples R China

This paper discusses a kind of convex optimization problem considering noises from in-formation transmission in multi-agent systems. Different from previous works, we focus on the objective function which is a summation of strictly L-0(F)-convex functions under random inner space. Our system is described by Ito formula, which leads to that it is hard to calculate second-order derivative when designing the projected subgradient algorithm. It is shown that all states in stochastic system will converge to the unique optimal state in the polyhedric set constraint by adopting projected subgradient algorithm and the con-vergence rate is also investigated. Numerical examples are provided to demonstrate the results.& nbsp;(c) 2021 Elsevier Inc. All rights reserved.

关键词： distributed convex optimization Projected subgradient algorithm Additive noise Polyhedric set constraint Random inner space

来源：评论

学校读者我要写书评

暂无评论

Finite-Time distributed convex optimization for Continuous-Time Multiagent Systems With Disturbance Rejection

引用

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2020年第2期7卷 686-698页

作者： Feng, Zhi Hu, Guoqiang Cassandras, Christos G. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Boston Univ Div Syst Engn Boston MA 02446 USA Boston Univ Ctr Informat & Syst Engn Boston MA 02446 USA

This paper presents continuous distributed algorithms to solve the finite-time distributed convex optimization problems of multiagent systems in the presence of disturbances. The objective is to design distributed algorithms such that a team of agents seeks to minimize the sum of local objective functions in a finite-time and robust manner. Specifically, a distributed optimization algorithm, combined with a continuous integral sliding-mode control scheme, is proposed to solve this finite-time optimization problem, while rejecting local disturbance signals. The developed algorithm is further applied to solve economic dispatch and resource allocation problems, and proven that under proposed schemes, the optimal solution can be achieved in finite time, while satisfying both global equality and local inequality constraints. Examples and numerical simulations are provided to show the effectiveness of the proposed methods.

关键词： distributed convex optimization disturbance rejection finite-time convergence multiagent system

来源：评论

学校读者我要写书评

暂无评论

Finite-Time Average Consensus Based Approach for distributed convex optimization

引用

ASIAN JOURNAL OF CONTROL 2020年第1期22卷 323-333页

作者： Ma, Wenlong Fu, Minyue Cui, Peng Zhang, Huanshui Li, Zhipeng Shandong Univ Sch Control Sci & Engn Jinan 250061 Peoples R China Univ Newcastle Sch Elect Engn & Comp Callaghan NSW 2308 Australia Guangdong Unvers Technol Sch Automat Guangzhou Peoples R China

In this paper, we consider a distributed convex optimization problem where the objective function is an average combination of individual objective function in multi-agent systems. We propose a novel Newton Consensus method as a distributed algorithm to address the problem. This method utilises the efficient finite-time average consensus method as an information fusion tool to construct the exact Newtonian global gradient direction. Under suitable assumptions, this strategy can be regarded as a distributed implementation of the classical standard Newton method and eventually has a quadratic convergence rate. The numerical simulation and comparison experiment show the superiority of the algorithm in convergence speed and performance.

关键词： Newton method finite-time average consensus distributed convex optimization multi-agent systems quadratic convergence

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：