In this article, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challenging scenario in which objective fu...
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In this article, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. We consider the challenging scenario in which objective functions are unknown and agents have only access to local measurements of their local functions. We propose a novel distributed algorithm that combines a recent gradient tracking policy with an extremum seeking technique to estimate the global descent direction. The joint use of these two techniques results in a distributed optimization scheme that provides arbitrarily accurate solution estimates through the combination of Lyapunov and averaging analysis approaches with consensus theory. We perform numerical simulations in a personalized optimization framework to corroborate the theoretical results.
In Wireless Multi-hop Networks (WMhNs), nodes whose absence significantly weakens network connectivity or partitions the network into disconnected components are called critical nodes. This paper focuses on a critical...
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In Wireless Multi-hop Networks (WMhNs), nodes whose absence significantly weakens network connectivity or partitions the network into disconnected components are called critical nodes. This paper focuses on a critical node problem, the Maximizing the Number of Connected Components (MaxNum) problem, which aims to identify critical nodes whose removal maximizes the number of connected components. Although the MaxNum problem is a well-known NP-Hard problem with various real-world applications, no distributed algorithm has been proposed to solve it. To address this gap, we propose an efficient distributed algorithm for the MaxNum problem in WMhNs. The algorithm uses a distributed depth-first search tree to identify critical nodes, requiring a bit complexity of ( x x log2 ) and a space complexity of ( + ), where denotes the maximum node degree. We evaluated the proposed algorithm through simulations and testbed networks, comparing it to the Linear Programming (LP) approach. Our findings show that the proposed distributed algorithm achieves promising outcomes, reaching nearly 90% of the optimal solution while reducing data transmission to half of that required by the centralized LP.
This article explores the variational generalized Nash equilibrium seeking for a class of fuzzy aggregative games, where each player's cost function is fuzzy and nonsmooth, and the strategy profile is constrained ...
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This article explores the variational generalized Nash equilibrium seeking for a class of fuzzy aggregative games, where each player's cost function is fuzzy and nonsmooth, and the strategy profile is constrained by a coupling nonlinear inequality, a affine coupling equality with alpha-cuts and heterogeneous local convex sets. Under the differential inclusion framework, a continuous-time distributed algorithm with a derivative feedback term is proposed. Benefiting from this algorithm, each player is assigned several auxiliary variables to share with its neighbors and estimate the aggregate function, so that the important information such as cost functions, constraints, and decisions are privatized. Based on fuzzy optimization and nonsmooth analysis, the asymptotic convergence of the algorithm to generalized Nash equilibrium is rigorously proved, and the convergence is not affected by the initial strategy profile of the players. Moreover, two application instances are used to demonstrate the theoretical results.
Efficient and distributed adaptive mesh construction and editing pose several challenges, including selecting the appropriate distributed data structure, choosing strategies for distributing computational load, and ma...
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Efficient and distributed adaptive mesh construction and editing pose several challenges, including selecting the appropriate distributed data structure, choosing strategies for distributing computational load, and managing inter-processor communication. distributed Combinatorial Maps permit the representation and editing of distributed 3D meshes. This paper addresses computation load and expands communication aspects through volume transfer operation and repartitioning strategies. This work is the first one defining such transfer for cells of any topology. We demonstrate the benefits of our method by presenting a parallel adaptive hexahedral subdivision operation, involving fully generic volumes, in a process including a conversion to conformal mesh and surface fitting. Our experiments compare different strategies using multithreading and MPI implementations to highlight the benefits of volume transfer. Special attention has been paid to generic aspects and adaptability of the framework.
This paper studies the distributed stochastic Nash equilibrium seeking problem under heavy-tailed noises. Unlike the traditional stochastic Nash equilibrium algorithms, where the gradient noises are usually assumed to...
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This paper studies the distributed stochastic Nash equilibrium seeking problem under heavy-tailed noises. Unlike the traditional stochastic Nash equilibrium algorithms, where the gradient noises are usually assumed to have a bounded variance, we assume that the gradient noises can be heavytailed, which can have an unbounded variance. A distributed Nash equilibrium seeking law combining projected gradient descent and gradient clipping is proposed. Sufficient conditions on the step-sizes are given to guarantee almost sure and in mean square convergence to the Nash equilibrium of the game. A numerical example is given to show the effectiveness and efficiency of the algorithm. (c) 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
This article addresses the challenge of achieving dynamic weighted average consensus (DWAC) in multi-agent systems (MAS) with a continuous prescribed-time control scheme. Unlike traditional average consensus approache...
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This article addresses the challenge of achieving dynamic weighted average consensus (DWAC) in multi-agent systems (MAS) with a continuous prescribed-time control scheme. Unlike traditional average consensus approaches, where convergence time is often uncertain, our method allows the convergence time to be precisely predetermined, also ensuring robustness against network disruptions. Additionally, the proposed controller operates continuously over the prescribed time period, avoiding the chattering phenomenon that can arise in discontinuous control methods. Furthermore, the protocol is fully distributed, employing adaptive control techniques that eliminate the need for prior knowledge of the communication topology. Numerical simulations are also provided to illustrate the effectiveness of the proposed control scheme.
The multi-agent characteristics of integrated energy systems are becoming increasingly prominent, rendering the energy management problems more intricate. Additionally, the distributed agents are faced with challenges...
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The multi-agent characteristics of integrated energy systems are becoming increasingly prominent, rendering the energy management problems more intricate. Additionally, the distributed agents are faced with challenges from the communication layer, such as packet losses and privacy disclosure issues. Therefore, a privacy-preserving neurodynamic-based optimization strategy considering packet losses is proposed in this article. A privacy-preserving communication model is constructed based on differential privacy (DP). Unlike traditional DP methods, the privacy preservation mechanism employed in this article can achieve a quantified high level of privacy while ensuring the solution accuracy of the optimization algorithm. Moreover, communication packet loss models based on both the two-state Markov process and the Bernoulli process are established. The proposed fully distributed scheme only requires communication between adjacent agents. The efficiency of the neurodynamic-based approach, the high-level privacy preservation, and the robustness against communication packet loss are demonstrated through several case studies.
This paper addresses a class of optimization problems with time-varying cost functions by proposing a fully distributed prescribed-time algorithm. The algorithm decomposes the overall optimization problem into three s...
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This paper addresses a class of optimization problems with time-varying cost functions by proposing a fully distributed prescribed-time algorithm. The algorithm decomposes the overall optimization problem into three successive subproblems, which are solved sequentially. During the three stages of the algorithm, the estimation of the total cost function's average gradient information, consensus among the states, and tracking of the optimal state trajectories are achieved in turn. Given the segmentation strategy's demand for rapid convergence, the algorithm ensures convergence within a prescribed time. Using the Lyapunov method, it is shown that all three subproblems can be solved within any user-prescribed time, independent of the system's initial states or topology. To further exploit the independence of prescribed-time convergence from system states, the algorithm eliminates the reliance on system topology information in parameter settings by introducing adaptive parameters in place of traditional fixed ones, thus enabling fully distributed control. Finally, numerical simulations and an UAV target tracking experiment are conducted to validate the effectiveness and practicality of the proposed algorithm.
As a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely ...
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As a crucial component of modern energy systems, wind energy plays a significant role in energy transition. In traditional wind power systems, mutual interference between wind turbines leads to wake effect, adversely impacts the power generation efficiency of wind farms. Herein, a multicluster distributed optimization strategy based on wake-DBSCAN, specifically designed for environments affected by wake interference is proposed. First, clustering analysis on the wind turbine layout and wind conditions to establish a foundation for efficient distributed computation is performed. Based on the clustering results, wake analysis is conducted to plan and optimize the operational strategy for each wind turbine cluster, resulting in a distributed optimization strategy for their operation. Additionally, simulation experiments are conducted on micrositing and time-varying wind conditions using real-world data from a wind farm in the Arua region. The experimental results demonstrate that the proposed algorithm can effectively improve the computational efficiency of wake optimization, while ensuring the effect of the wake optimization algorithm in actual wind farms as much as possible, which is more in line with the wake optimization needs of actual wind farms. The algorithm proposed in this article provides valuable insights into wind turbine operation and maintenance under time-varying conditions.
This paper studies the distributed Nash equilibrium seeking problem for players subject to unknown nonlinear dynamics and input delay. By designing a distributed estimator for each player to estimate other players'...
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This paper studies the distributed Nash equilibrium seeking problem for players subject to unknown nonlinear dynamics and input delay. By designing a distributed estimator for each player to estimate other players' decisions and embedding an auxiliary variable to compensate for the influence of unknown nonlinearities, the distributed Nash equilibrium seeking algorithms are obtained for first-, second-, and high-order nonlinear players, respectively. With the help of the Lyapunov stability theory and Lyapunov-Krasovskii functional approach, the maximum allowable input delay is determined and the global asymptotic convergence of players' decisions to the Nash equilibrium is proved. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed methods.
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