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A Fully distributed Approach to Optimal Energy Scheduling of Users and Generators Considering a Novel Combined Neurodynamic algorithm in Smart Grid

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1325-1335页

作者： Chentao Xu Xing He the Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information EngineeringSouthwest UniversityChongqing 400715China

A fully distributed microgrid system model is presented in this *** the user side,two types of load and plug-in electric vehicles are considered to schedule energy for more *** charging and discharging states of the electric vehicles are represented by the zero-one variables with more *** solve the nonconvex optimization problem of the users,a novel neurodynamic algorithm which combines the neural network algorithm with the differential evolution algorithm is designed and its convergence speed is faster.A distributed algorithm with a new approach to deal with the inequality constraints is used to solve the convex optimization problem of the generators which can protect their *** results and comparative experiments show that the model and algorithms are effective.

关键词： Differential evolution algorithm distributed algorithm electric vehicle neural network zero-one variable.

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distributed algorithmS WITH FINITE DATA RATES THAT SOLVE LINEAR EQUATIONS

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SIAM JOURNAL ON OPTIMIZATION 2020年第2期30卷 1191-1222页

作者： Lei, Jinlong Yi, Peng Shi, Guodong Anderson, Brian D. O. Tongji Univ Dept Control Sci & Engn Shanghai Peoples R China Tongji Univ Shanghai Inst Intelligent Sci & Technol Shanghai Peoples R China Univ Sydney Sch Aerosp Mech & Mechatron Engn Australian Ctr Field Robot Sydney NSW 2006 Australia Hangzhou Dianzi Univ Hangzhou Peoples R China Australian Natl Univ Res Sch Elect Energy & Mat Engn Canberra ACT 0200 Australia

In this paper, we study network linear equations subject to digital communications with a finite data rate, where each node is associated with one equation from a system of linear equations. Each node holds a dynamic state and interacts with its neighbors through an undirected connected graph, where along each link the pair of nodes share information. Due to the data rate constraint, each node builds an encoder/decoder pair, with which it produces transmitted messages with a zooming-in finite-level uniform quantizer and also generates estimates of its neighbors' states from the received signals. We then propose a distributed quantized algorithm and show that when the network linear equations admit a unique solution, each node's state is driven to that solution exponentially fast. We further analyze the asymptotic rate of convergence and show that a larger number of quantization levels leads to a faster convergence rate although the rate is still fundamentally bounded by the inherent network structure and the linear equations. In addition, we establish a bound on the total number of communication bits required to obtain a solution with a prescribed accuracy. When a unique least-squares solution exists, we show that the algorithm can compute such a solution with a suitably selected time-varying step-size inherited from the encoder and zooming-in quantizer dynamics. In both cases, a minimal data rate is shown to be enough for guaranteeing the desired convergence when the algorithm parameters are properly chosen. These results ensure the applicability of various network linear equation solvers when peer-to-peer communication is digital.

关键词： network linear equations data rate quantization distributed algorithm

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Prescribed-time distributed resource allocation algorithm for heterogeneous linear multi-agent networks with unbalanced directed communication

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APPLIED MATHEMATICS AND COMPUTATION 2025年 503卷

作者： Lian, Yuxiao Zhang, Baoyong Yuan, Deming Yao, Yao Song, Bo Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Jiangsu Peoples R China Jiangsu Normal Univ Sch Elect Engn & Automat Xuzhou 221116 Jiangsu Peoples R China

In this paper, a prescribed-time distributed algorithm is proposed to solve the resource allocation problem among the heterogeneous linear multi-agent systems over unbalanced directed networks. First, an estimator with prescribed-time convergence performance is designed to cope with the asymmetry of the unbalanced network topology. Then, a novel prescribed-time convergence result that features an adjustable convergence rate is developed. Based on this result, it is shown that the algorithm developed in this paper ensures the agents' outputs accurately reach the optimal solution within a prescribed-time and they are maintained at the optimum thereafter. Furthermore, a parameter selection rule is formulated to reflect the low conservatism of the algorithm. This indicates that the parameters affecting the convergence speed of the algorithm are not necessary to rely on the global information. Finally, the performance of the proposed algorithm is illustrated through simulations.

关键词： Resource allocation distributed algorithm Heterogeneous multi-agent systems Unbalanced directed networks Prescribed-time convergence

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distributed Conditional Gradient algorithm for Two-Network Saddle-point Problem 34

Distributed Conditional Gradient Algorithm for Two-Network S...

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34th Chinese Control and Decision Conference (CCDC)

作者： Hou, Jie Zeng, Xianlin Beijing Inst Technol Sch Automat Key Lab Intelligent Control & Decis Complex Syst Beijing Peoples R China

ISBN: (纸本)9781665478960

We consider a two-network saddle-point problem with constraints, whose projections are expensive. We propose a projection-free algorithm, which is referred to as distributed Frank-Wolfe Saddle-Point algorithm (DFWSP), which combines the gradient tracking technique and Frank-Wolfe technique. We prove that the algorithm achieves O(1/k(2)) convergence rate for strongly-convex-strongly-concave saddle-point problems. We empirically shows that the proposed algorithm has better numerical performance than the distributed projected saddle-point algorithm.

关键词： Saddle-Point Problem distributed algorithm Projection-Free Method Convergence Rate

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A distributed algorithm for Large-Scale Graph Partitioning

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ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS 2015年第2期10卷 12-12页

作者： Rahimian, Fatemeh Payberah, Amir H. Girdzijauskas, Sarunas Jelasity, Mark Haridi, Seif KTH Royal Inst Technol Stockholm Sweden SICS Swedish ICT SE-16429 Kista Sweden Hungarian Acad Sci MTA SZTE Res Grp AI H-6701 Szeged Hungary Univ Szeged H-6701 Szeged Hungary

Balanced graph partitioning is an NP-complete problem with a wide range of applications. These applications include many large-scale distributed problems, including the optimal storage of large sets of graph-structured data over several hosts. However, in very large-scale distributed scenarios, state-of-the-art algorithms are not directly applicable because they typically involve frequent global operations over the entire graph. In this article, we propose a fully distributed algorithm called JA-BE-JA that uses local search and simulated annealing techniques for two types of graph partitioning: edge-cut partitioning and vertex-cut partitioning. The algorithm is massively parallel: There is no central coordination, each vertex is processed independently, and only the direct neighbors of a vertex and a small subset of random vertices in the graph need to be known locally. Strict synchronization is not required. These features allow JA-BE-JA to be easily adapted to any distributed graph-processing system from data centers to fully distributed networks. We show that the minimal edge-cut value empirically achieved by JA-BE-JA is comparable to state-of-the-art centralized algorithms such as METIS. In particular, on large social networks, JA-BE-JA outperforms METIS. We also show that JA-BE-JA computes very low vertex-cuts, which are proved significantly more effective than edge-cuts for processing most real-world graphs.

关键词： Design algorithms Performance graph partitioning edge-cut partitioning vertex-cut partitioning distributed algorithm load balancing simulated annealing

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Learning from vertically distributed data across multiple sites: An efficient privacy-preserving algorithm for Cox proportional hazards model with variable selection

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JOURNAL OF BIOMEDICAL INFORMATICS 2024年 149卷 104581页

作者： Miao, Guanhong Yu, Lei Yang, Jingyun Bennett, David A. Zhao, Jinying Wu, Samuel S. Univ Florida Coll Publ Hlth & Hlth Profess Dept Epidemiol Gainesville FL 32610 USA Univ Florida Coll Med Gainesville FL 32610 USA Univ Florida Ctr Genet Epidemiol & Bioinformat Gainesville FL 32610 USA Univ Florida Coll Publ Hlth & Hlth Profess Dept Biostat Gainesville FL 32610 USA Rush Univ Rush Alzheimers Dis Ctr Med Ctr Chicago IL USA Rush Univ Med Ctr Dept Neurol Sci Chicago IL USA Univ Florida Dept Stat 2004 Mowry RdCTRB Room 5243 Gainesville FL 32610 USA

Objective: To develop a lossless distributed algorithm for regularized Cox proportional hazards model with variable selection to support federated learning for vertically distributed ***: We propose a novel distributed algorithm for fitting regularized Cox proportional hazards model when data sharing among different data providers is restricted. Based on cyclical coordinate descent, the proposed algorithm computes intermediary statistics by each site and then exchanges them to update the model parameters in other sites without accessing individual patient-level data. We evaluate the performance of the proposed algorithm with (1) a simulation study and (2) a real-world data analysis predicting the risk of Alzheimer's dementia from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Moreover, we compared the performance of our method with existing privacy-preserving ***: Our algorithm achieves privacy-preserving variable selection for time-to-event data in the vertically distributed setting, without degradation of accuracy compared with a centralized approach. Simulation dem-onstrates that our algorithm is highly efficient in analyzing high-dimensional datasets. Real-world data analysis reveals that our distributed Cox model yields higher accuracy in predicting the risk of Alzheimer's dementia than the conventional Cox model built by each data provider without data sharing. Moreover, our algorithm is computationally more efficient compared with existing privacy-preserving Cox models with or without regularization ***: The proposed algorithm is lossless, privacy-preserving and highly efficient to fit regularized Cox model for vertically distributed data. It provides a suitable and convenient approach for modeling time-to-event data in a distributed manner.

关键词： distributed algorithm Cox proportional hazards model Privacy preserving Variable selection Vertical partitioning

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A Multi-Stage Graph Aided algorithm for distributed Service Function Chain Provisioning Across Multiple Domains

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IEEE ACCESS 2021年 9卷 114884-114904页

作者： Kibalya, Godfrey Serrat-Fernandez, Joan Gorricho, Juan-Luis Bujjingo, Doreen Gift Serugunda, Jonathan Univ Politecn Cataluna Dept Network Engn Barcelona 08034 Spain Makerere Univ Dept Elect & Comp Engn Kampala Uganda

Network Service Providers (NSPs) envisage to support the divergent and stringent requirements of future services by instantiating these services as service chains, commonly referred to as Service Function Chains (SFCs), that are customized and configured to meet specific service requirements. However, due to the limited footprint of the Infrastructure Providers (InPs), these SFCs may have to transcend multiple InPs/domains. In this regard, determining the optimal set of InPs in which to embed the SFC request emerges as a complex problem for several reasons. First, the large number of possible combinations for selecting the InPs to embed the different sub-chains of the request makes this problem computationally complex, rendering optimal solutions only after long computations, especially in large scale networks, which is unfeasible for delay sensitive applications. Second, the unwillingness of InPs to disclose their internal information, which may be vital for making embedding decisions, usually implies the provisioning of single-domain solutions, which are unsuitable in this working scenario. In this regard, this paper first formulates the multi-domain service deployment problem under multiple request constraints, such as bandwidth or delay, among others. Then, due to the NP-hardness nature of the above problem, this paper proposes an algorithm that is aided by a multi-stage graph for computing a request embedding solution in a distributed manner, solving the problem in acceptable run-times. Results from different simulations reveal that the proposed algorithm is optimized in terms of acceptance ratio and embedding cost, with up to 60.0% and 88.7% improvements in terms of embedding cost and execution time, respectively, for some scenarios, in comparison with a benchmark state-of-the-art algorithm.

关键词： Indium phosphide III-V semiconductor materials Delays Computer architecture distributed algorithms Service function chaining Licenses Service function chaining distributed algorithm multi-domain embedding network function virtualization

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distributed Event-Triggered algorithm with Network Independent Step-Size for Constraint-Coupled Optimization Problems

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第12期33卷 2450222-2450222页

作者： Chen, Baitong Yang, Jianhua Lu, Wei Pedrycz, W. Sun, Changhai Dalian Univ Technol Sch Control Sci & Engn Dalian Liaoning Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada Dalian Univ Technol Sch Elect Engn Dalian Liaoning Peoples R China

In this study, we introduce a distributed algorithm that is specifically designed to address optimization problems featuring a decomposable objective function and equality constraints. To minimize the amount of communication required, we incorporate an event-triggered mechanism that enables information exchange only when variable values exceed predefined thresholds. Importantly, our proposed algorithm possesses a distinctive characteristic where the determination of step size is solely based on the properties of the objective function, regardless of the structure of the communication network. Even in situations where changes occur in the network structure, our algorithm remains valid without necessitating any updates to its step size. Assuming strong convexity and smoothness in local objective functions, along with appropriate event-triggered thresholds, our algorithm achieves a convergence rate that is linear. Several numerical experiments provide evidence supporting the effectiveness and superiority of our proposed approach.

关键词： Constrained optimization distributed algorithm event-triggered communication linear convergence network independent step-size

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distributed Soft Clustering algorithm for IoT Based on Finite Time Average Consensus

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IEEE INTERNET OF THINGS JOURNAL 2021年第21期8卷 16096-16107页

作者： Yu, Hao Chen, Hui Zhao, Shengjie Shi, Qingjiang Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Tongji Univ Sch Math Sci Shanghai 200092 Peoples R China Shenzhen Res Inst Big Data Shenzhen Peoples R China

Clustering is a common technique for statistical data analysis and it has been widely used in many fields. This article investigates data clustering over the Internet-of-Things (IoT) network. Facing the IoT network challenges, including data volume, communication latency, and information security, we here propose a distributed soft clustering algorithm for the IoT environments where each IoT node may have data from multiple clusters. Considering that the main task of soft clustering is to compute each cluster center in a weighted averaging fashion, our distributed clustering method resorts to an efficient finite-time average-consensus algorithm. Moreover, to make the distributed clustering algorithm more stable and be able to escape from some bad local optimum, we propose a distributed deterministic initialization method based on data variance partitioning. Experiments show that the proposed distributed soft clustering algorithm can offer the same performance as its centralized counterpart in terms of both convergence and clustering quality. Besides, unlike most clustering methods relying on probabilistic initialization, our algorithm could provide stable clustering quality which makes it more suitable for IoT networks. A real-world case study about the clustering analysis for distributed data sets collected by environmental monitoring stations is offered, which shows the potential of our algorithms in practical applications.

关键词： Average consensus clustering algorithm distributed algorithm fuzzy C-means Internet of Things (IoT) K-means

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A distributed Double-Newton Descent algorithm for Cooperative Energy Management of Multiple Energy Bodies in Energy Internet

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 5993-6003页

作者： Li, Yushuai Gao, David Wenzhong Gao, Wei Zhang, Huaguang Zhou, Jianguo Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Univ Denver Dept Elect & Comp Engn Denver CO 80208 USA Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110004 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110004 Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Tsinghua SIGS Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518055 Peoples R China

This article investigates the problem of distributed cooperative energy management of multiple energy bodies with the consideration of both the optimal energy generation/consumption of each participant within single energy body and the optimal energy distribution on the interconnected lines between any pair of energy bodies. First, we define the physical and communication structure of the system formed by many energy bodies, each of which is viewed as a multienergy prosumer. Then, a distributed energy management model is proposed to achieve not only maximum profits of overall energy generation and consumption, but also minimum cost of energy delivery. To address this issue, a distributed double-Newton descent (DDND) algorithm is proposed, which possesses two advantages. On the one hand, by employing second-order information, the concept of Newton descent is embedded into the implementation of the proposed algorithm, resulting in faster convergence speed. On the other hand, the proposed algorithm performs in a fully distributed fashion. As a consequence, each participant can locally obtain its optimal operation as well as the global energy market clearing prices;meanwhile, each energy router can locally obtain the optimal exchanged energy with its neighbor energy routers. Moreover, we prove that the proposed DDND algorithm can asymptotically converge to the global optimal point. As a result, the correctness of the DDND algorithm can be guaranteed in theory. Finally, simulation results validate the effectiveness of the proposed algorithm.

关键词： Energy management Microgrids Smart grids Informatics distributed algorithms Economics Pipelines distributed algorithm energy body (EB) energy internet (EI) Newton descent

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