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A general framework and decentralised algorithms for collective computational processes

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2024年 158卷 11-27页

作者： Audrito, Giorgio Casadei, Roberto Torta, Gianluca Univ Torino Turin Italy Alma Mater Studiorum Univ Bologna Bologna Italy

Recent research on collective adaptive systems and macro-programming has shown the importance of programming abstractions for expressing the self-organising behaviour of ensembles, large and dynamic sets of collaborating devices. These generally leverage the interplay between the execution model and the program logic to steer the global-level emergent behaviour of the system. One notable example is the aggregate process abstraction: in an asynchronous round-based computational model, it allows to specify how aggregate-level computations are spawned, take form or spread on a domain of devices, and ultimately quit. Previous presentations of aggregate processes, however, are given in the formal framework of the field calculus, requiring knowledge of its syntax and articulated semantics. To provide a more accessible and language-agnostic presentation of such an abstraction, in this paper we introduce a general formal framework of collective computational processes (CCP). Specifically, as key contribution, we model and describe the programming interface ( spawn construct) and dynamics of CCPs on event structures. Furthermore, we also propose novel algorithms for efficient propagation and termination of CCPs, based on statistics on the information speed and a notion of progressive wave-like closure. Crucially, thanks to our theoretical framework, we can provide optimality guarantees for the proposed algorithms, whose performance, superior to the state of the art, is assessed by simulation. Finally, to show applicability of CCPs, we provide a case study of situated service discovery in peer-to-peer networks.

关键词： Collective adaptive systems Collective computing Dynamic ensembles Aggregate processes Decentralised systems distributed algorithms Neighbourhood communications

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An optimization scheme for vehicular edge computing based on Lyapunov function and deep reinforcement learning

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IET COMMUNICATIONS 2024年第15期18卷 908-924页

作者： Zhu, Lin Tan, Long Li, Bingxian Tian, Huizi Heilongjiang Univ Sch Comp Sci & Technol 74Xuefu Rd Harbin 150080 Heilongjiang Peoples R China

Traditional vehicular edge computing research usually ignores the mobility of vehicles, the dynamic variability of the vehicular edge environment, the large amount of real-time data required for vehicular edge computing, the limited resources of edge servers, and collaboration issues. In response to these challenges, this article proposes a vehicular edge computing optimization scheme based on the Lyapunov function and Deep Reinforcement Learning. In this solution, this article uses Digital Twin technology (DT) to simulate the vehicular edge environment. The edge server DT is used to simulate the vehicular edge environment under the edge server, and the base station DT is used to simulate the entire vehicular edge system environment. Based on the real-time data obtained from DT simulation, this paper defines the Lyapunov function to simplify the migration cost of vehicle tasks between servers into a multi-objective dynamic optimization problem. It solves the problem by applying the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm. Experimental results show that compared with other algorithms, this scheme can effectively optimize the allocation and collaboration of vehicular edge computing resources and reduce the delay and energy consumption caused by vehicle task processing. This article proposes an optimization scheme for vehicular edge computing based on the Lyapunov function and Deep Reinforcement Learning. First, a DT-assisted vehicular edge system was designed, using DT technology to simulate the vehicular edge environment and obtain real-time data for DRL training. Second, the vehicular edge computing offloading problem is formalized using a Markov decision process, and the Lyapunov function is used to simplify the migration cost constraint of tasks between edge servers into a multi-objective constraint problem to balance the load between edge servers. Finally, the TD3 algorithm is used to solve the offloading optimization problem of vehicle ta

关键词： 5G mobile communication cloud computing computer networks distributed algorithms Internet of Things Markov processes mobile computing mobile communication neural nets

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Optimal distributed Composite Testing in High-Dimensional Gaussian Models With 1-Bit Communication

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第6期68卷 4070-4084页

作者： Szabo, Botond Vuursteen, Lasse Van Zanten, Harry Bocconi Univ Bocconi Inst Data Sci & Analyt BIDSA Dept Decis Sci I-20100 Milan Italy Delft Univ Technol Delft Inst Appl Math DIAM NL-2628 CD Delft Netherlands Vrije Univ Amsterdam Dept Math NL-1081 HV Amsterdam Netherlands

In this paper we study the problem of signal detection in Gaussian noise in a distributed setting where the local machines in the star topology can communicate a single bit of information. We derive a lower bound on the Euclidian norm that the signal needs to have in order to be detectable. Moreover, we exhibit optimal distributed testing strategies that attain the lower bound.

关键词： Testing Estimation Complexity theory Signal to noise ratio distributed databases Mutual information Gaussian noise Testing distributed algorithms hypothesis testing minimax lower bounds Gaussian noise federated learning

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Decentralized management of unbalanced multi-agent distribution systems: A transactive approach leveraging ADMM framework

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IET GENERATION TRANSMISSION & DISTRIBUTION 2025年第1期19卷

作者： Fattaheian-Dehkordi, Sajjad Aghaei, Jamshid Amjady, Nima Aalto Univ Dept Elect Engn & Automat Espoo Finland Cent Queensland Univ Sch Engn & Technol Rockhampton Qld Australia Federat Univ Australia Ctr New Energy Transit Res Ballarat Vic Australia

The emergence of multi-agent systems and significant integration of distributed energy sources (DESs) are transforming distribution networks. This necessitates a decentralized management strategy for unbalanced operation in multi-agent distribution systems (MADSs) due to the autonomous nature and potential for unbalanced injection of single-phase DESs. Accordingly, this paper proposes a novel approach for decentralized management of unbalanced operation in MADSs. The approach leverages a customized alternating direction method of multipliers to facilitate decentralized decision-making, while incorporating transactive energy signals aligned with the alternating direction method of multiplier framework to enable independent agent operation. In this scheme, independent agents would optimize their operating costs considering the announced transactive signals, which model the power prices and power loss in the grid. The decentralized structure enables agents to apply stochastic and condition value at risk methods to address the uncertainty and associated risk in scheduling resources. Furthermore, without violating the privacy concerns of agents, the developed transactive-based scheme facilitates minimizing the asymmetrical condition, caused by the unbalanced integration of DESs, in the power request at the connection point of MADSs and transmission networks. Finally, the proposed methodology is simulated on 37-bus and 123-bus test-systems to study its effectiveness in managing the MADSs with unbalanced integration of DESs.

关键词： distribution networks multi-agent systems renewable energy sources decentralised control distributed algorithms distributed control

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distributed generalized Nash equilibrium seeking for noncooperative games with unknown cost functions

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL 2022年第16期32卷 8948-8964页

作者： Cai, Xin Xiao, Feng Wei, Bo North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102206 Peoples R China Xinjiang Univ Sch Elect Engn Urumpqi Peoples R China

In this article, a distributed nonmodel based generalized Nash equilibrium (GNE) seeking algorithm is proposed for a class of constrained noncooperative games with unknown cost functions. In the game, the strategy of each agent is restricted by both the coupled equality constraint and local inequality constraints. By virtue of the exact penalty method, an auxiliary cost function is constructed with the cost function and the local constraints. The main feature of the proposed algorithm depends on the capability to estimate the gradient information of auxiliary cost functions with only the values of costs. This is obtained by the extremum seeking control (ESC). To deal with the coupled constraints, only the Lagrange multiplier is transmitted among agents with some prior information about the coupled constraints. Moreover, a diminishing dither signal is introduced in the seeking algorithm to remove undesirable steady-state oscillations occurred in the classical ESC. As a result, the nonlocal convergence of the designed seeking algorithm to the GNE of the game is obtained by the singular perturbation theory, averaging analysis and Lyapunov stability theory. Numerical examples are given to verify the effectiveness of our proposed method.

关键词： distributed algorithms extremum seeking control generalized Nash equilibrium noncooperative games

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distributed distance domination in graphs with no K2,t-minor

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THEORETICAL COMPUTER SCIENCE 2022年 916卷 22-30页

作者： Czygrinow, Andrzej Hanckowiak, Michal Witkowski, Marcin Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA Adam Mickiewicz Univ Fac Math & Comp Sci Poznan Poland

We prove that a simple distributed algorithm finds a constant approximation of an optimal distance-k dominating set in graphs with no K2,t-minor. The algorithm runs in a constant number of rounds. We further show how this procedure can be used to give a distributed algorithm which given c > 0 and k, t is an element of Z+ finds in a graph G = (V, E) with no K2,t-minor a distance-k dominating set of size at most (1 + c) of the optimum. The algorithm runs in O (loge | V |) rounds in the Local model. In particular, both algorithms work in outerplanar graphs.

关键词： distributed algorithms Distance dominating set Sparse graphs Local model

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Swarm intelligence-based framework for accelerated and optimized assembly line design in the automotive industry

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JOURNAL OF INTELLIGENT MANUFACTURING 2024年第6期35卷 2829-2843页

作者： El Houd, Anass Piranda, Benoit De Matos, Raphael Bourgeois, Julien Univ Franche Comte FEMTO ST Inst CNRS F-25200 Montbeliard France Forvia Clean Mobil R&D Ctr F-25250 Bavans France

This study proposes a dynamic simulation-based framework that utilizes swarm intelligence algorithms to optimize the design of hybrid assembly lines in the automotive industry. Two recent discrete versions of Whale Optimization Algorithm (named VNS-DWOA) and Gorilla Troops Optimizer (named DGTO) were developed to solve the assembly line balancing problem. The effectiveness of these algorithms was compared to six conventional meta-heuristics as well as the solution proposed by process design experts. The experimental results show that our methods outperform the conventional meta-heuristics and achieve comparable or better results than the experts' solution. Particulary, VNS-DWOA, being the top performer, has consistently provided averagely remarkable enhancements of cycle time, ranging from 7% when compared to the process expert's solution to 20% maximum improvement compared to all other methods. The findings of this study highlight the effectiveness of utilizing swarm intelligence algorithms and dynamic simulation-based frameworks as well as the potential benefits of implementing these digital methods in industrial settings, as they can significantly accelerate and enhance the optimization of assembly line design particularly and reduce time to market generally.

关键词： Industry 4 0 Assembly line balancing Swarm intelligence Discrete metaheuristics Combinatorial optimization distributed algorithms

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distributed Optimization Design for Computation of Algebraic Riccati Inequalities

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1924-1935页

作者： Zeng, Xianlin Chen, Jie Hong, Yiguang Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing 100081 Peoples R China Beijing Inst Technol Minist Educ Key Lab Biomimet Robots & Syst Beijing Adv Innovat Ctr Intelligent Robots & Syst Beijing 100081 Peoples R China Tongji Univ Shanghai Res Inst Intelligent Autonomous Syst Dept Control Sci & Engn Shanghai 200092 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Key Lab Syst & Control Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Math Sci Beijing 100190 Peoples R China

This article proposes a distributed optimization design to compute continuous-time algebraic Riccati inequalities (ARIs), where the information of matrices is distributed among agents. We propose a design procedure to tackle the nonlinearity, the inequality, and the coupled information structure of ARI;then, we design a distributed algorithm based on an optimization approach and analyze its convergence properties. The proposed algorithm is able to verify whether ARI is feasible in a distributed way and converges to a solution if ARI is feasible for any initial condition.

关键词： Linear matrix inequalities distributed algorithms Optimization Heuristic algorithms Dynamical systems Convergence Multi-agent systems distributed algorithm distributed optimization matrix inequality

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distributed Optimization in Distribution Systems: Use Cases, Limitations, and Research Needs

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IEEE TRANSACTIONS ON POWER SYSTEMS 2022年第5期37卷 3469-3481页

作者： Patari, Niloy Venkataramanan, Venkatesh Srivastava, Anurag Molzahn, Daniel K. Li, Na Annaswamy, Anuradha Washington State Univ WSU Pullman WA 99164 USA Natl Renewable Energy Lab Golden CO 80401 USA West Virginia Univ Comp Sci & Elect Engn Dept Morgantown WV 26506 USA Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Harvard Univ Elect Engn & Appl Math Cambridge MA 02138 USA MIT Dept Mech Engn Act Adapt Control Lab Cambridge MA 02139 USA

Electric distribution grid operations typically rely on both centralized optimization and local non-optimal control techniques. As an alternative, distribution system operational practices can consider distributed optimization techniques that leverage communications among various neighboring agents to achieve optimal operation. With the rapidly increasing integration of distributed energy resources (DERs), distributed optimization algorithms are growing in importance due to their potential advantages in scalability, flexibility, privacy, and robustness relative to centralized optimization. Implementation of distributed optimization offers multiple challenges and also opportunities. This paper provides a comprehensive review of the recent advancements in distributed optimization for electric distribution systems and classifications using key attributes. Problem formulations and distributed optimization algorithms are provided for example use cases, including volt/var control, market clearing process, loss minimization, and conservation voltage reduction. Finally, this paper also presents future research needs for the applicability of distributed optimization algorithms in the distribution system.

关键词： Optimization Load flow Mathematical models distributed algorithms Voltage control Couplings Classification algorithms Optimal power flow distributed energy resources active distribution systems distributed optimization

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Simple Parallel and distributed algorithms for Spectral Graph Sparsification 14

Simple Parallel and Distributed Algorithms for Spectral Grap...

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26th ACM Symposium on Parallelism in algorithms and Architectures (SPAA)

作者： Koutis, Ioannis Univ Puerto Rico Rio Piedras Comp Sci Dept San Juan PR 00925 USA

ISBN: (纸本)9781450328210

We describe a simple algorithm for spectral graph sparsification, based on iterative computations of weighted spanners and uniform sampling. Leveraging the algorithms of Baswana and Sen for computing spanners, we obtain the first distributed spectral sparsification algorithm. We also obtain a parallel algorithm with improved work and time guarantees. Combining this algorithm with the parallel framework of Peng and Spielman for solving symmetric diagonally dominant linear systems, we get a parallel solver which is much closer to being practical and significantly more efficient in terms of the total work.

关键词： Parallel algorithms distributed algorithms Spectral Sparsification SDD linear systems

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