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Predefined-time convergence strategies for multi-cluster games in hybrid heterogeneous systems

NONLINEAR ANALYSIS-HYBRID SYSTEMS 2025年 55卷

作者： Niu, Fuxi Nian, Xiaohong Cent South Univ Sch Automat Changsha 410075 Peoples R China

This paper explores the problem of (generalized) Nash equilibrium search in multi-cluster games with heterogeneous dynamics and multiple constraints. Within this research framework, each agent acquires information solely through local interactions with its neighbors and forms clusters based on similarity of interests. These clusters manifest dual relationships of cooperation and competition: agents within the same cluster enhance decision-making capabilities through cooperation, while different clusters compete to maximize their respective benefits. To delve into these complex interactions among clusters and the learning and evolution processes among agents, four distributed control algorithms suitable for various scenario requirements are designed and implemented. These algorithms ensure that each agent converges to a Nash equilibrium (NE) or generalized Nash equilibrium (GNE) of the multi-cluster system within predefined time points. Finally, we apply these algorithms to the connectivity control problem of unmanned aerial vehicle swarms with diverse dynamics, validating the theoretical results through comprehensive simulations.

关键词： Multi-cluster interaction Strategy evolution distributed algorithm Predefined time convergence Hybrid heterogeneous dynamics

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GameTE: A Game-Theoretic distributed Traffic Engineering in Trustless Multi-Domain SDN 44

GameTE: A Game-Theoretic Distributed Traffic Engineering in ...

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44th IEEE International Conference on distributed Computing Systems (ICDCS)

作者： Liu, Yangyang Hua, Jingyu Zhang, Yuan Zhong, Sheng Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Peoples R China

ISBN: (纸本)9798350386066;9798350386059

With growing network service demands, rational and efficient multi-domain resource allocation is paramount. Research aims to develop intelligent Traffic Engineering (TE) algorithms that can dynamically allocate resources, adapt to changing conditions, and meet user needs. TE algorithms based on Software-Defined Networking (SDN) have proven effective for this goal by leveraging the centralized control plane and programmable data plane of SDN. This enables flexible and dynamic optimization of routing and resource allocation across multiple domains to meet traffic demands. However, domains operated by different service providers may exhibit non-cooperative behavior due to conflicts of interest and competition. Some domains may act selfishly by hiding bandwidth or exaggerating inter-domain requests to reserve more resources for themselves. This complicates TE design as algorithms can no longer assume universal cooperation in multi-domain networks. Game theory provides a framework to model competition between domains through behaviors like request forwarding, dropping and study cooperation strategies. This paper presents GameTE, a game-theoretic distributed TE algorithm for multi-domain SDN environments without trusted relationships between domains. By incorporating incentives and punishments, our algorithm suppresses selfish behaviors and promotes efficient resource utilization. Evaluation results demonstrate that GameTE is effective in curbing deception, enhancing resource sharing between domains, and improving overall network performance compared to baseline schemes.

关键词： SDN game theory distributed algorithm traffic engineering trust

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Asynchronous distributed algorithms for Seeking Generalized Nash Equilibria Under Full and Partial-Decision Information

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IEEE TRANSACTIONS ON CYBERNETICS 2020年第6期50卷 2514-2526页

作者： Yi, Peng Pavel, Lacra Washington Univ Dept Elect & Syst Engn St Louis MO 63130 USA Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G9 Canada

This paper investigates asynchronous algorithms for distributedly seeking generalized Nash equilibria with delayed information in multiagent networks. In the game model, a shared affine constraint couples all players' local decisions. Each player is assumed to only access its private objective function, private feasible set, and a local block matrix of the affine constraint. We first give an algorithm for the case when each agent is able to fully access all other players' decisions. By using auxiliary variables related to communication links and the edge Laplacian matrix, each player can carry on its iteration asynchronously with only private data and possibly delayed information from its neighbors. Then, we consider the case when agents cannot know all other players' decisions, called a partial-decision information case. We introduce a local estimation of the overall agents' decisions and incorporate consensus dynamics on these local estimations. The two algorithms do not need any centralized clock coordination, fully exploit the local computation resource, and remove the idle time due to waiting for the "slowest" agent. Both algorithms are developed by preconditioned forward-backward operator splitting, and their convergence is shown by relating them to asynchronous fixed-point iterations, under proper assumptions and fixed and nondiminishing step-size choices. Numerical studies verify the algorithms' convergence and efficiency.

关键词： Games Convergence Heuristic algorithms distributed algorithms Linear programming Estimation Couplings Asynchronous computation distributed algorithm generalized Nash equilibrium (GNE) monotone game multiagent systems operator splitting methods

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A Fully distributed Hybrid Control Framework For Non-Differentiable Multi-Agent Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第10期9卷 1792-1800页

作者： Xia Jiang Xianlin Zeng Jian Sun Jie Chen Yue Wei the Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081 the Beijing Institute of Technology Chongqing Innovation Center Chongqing 401120China IEEE the School of Electronic and Information Engineering Tongji UniversityShanghai 200082 the Peng Cheng Laboratory Shenzhen 518055China

This paper develops a fully distributed hybrid control framework for distributed constrained optimization *** individual cost functions are non-differentiable and *** on hybrid dynamical systems,we present a distributed state-dependent hybrid design to improve the transient performance of distributed primal-dual first-order optimization *** proposed framework consists of a distributed constrained continuous-time mapping in the form of a differential inclusion and a distributed discrete-time mapping triggered by the satisfaction of local jump *** the semistability theory of hybrid dynamical systems,the paper proves that the hybrid control algorithm converges to one optimal solution instead of oscillating among different *** simulations illustrate better transient performance of the proposed hybrid algorithm compared with the results of the existing continuous-time algorithms.

关键词： distributed algorithm hybrid framework multiagent network non-differentiable optimization

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Discrete evacuation in graphs with multiple exits

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THEORETICAL COMPUTER SCIENCE 2025年 1035卷

作者： Borowiecki, Piotr Das, Shantanu Dereniowski, Dariusz Kuszner, Lukasz Univ Zielona Gora Inst Control & Computat Engn Zielona Gora Poland Univ Toulon & Var Aix Marseille Univ CNRS LIS Marseille France Gdansk Univ Technol Fac Elect Telecommun & Informat Gdansk Poland Univ Gdansk Inst Informat Gdansk Poland

In this paper, we consider the problem of efficient evacuation of mobile agents from distinct nodes in a graph to multiple exit nodes, while avoiding congestion and bottlenecks, and minimizing the total evacuation time. Each node in the graph can only hold one agent at a time, so the agents must choose their movements based on the locations of other agents to optimize the evacuation process. We consider two scenarios: the centralized (offline) and the distributed (online) setting. In the former one, the agents have complete information about the initial positions of other agents. In the distributed setting, agents lack prior knowledge of other agents' locations but can communicate locally with nearby agents and must adapt their strategy in an online fashion as they move and gather more information. In this study, we propose an offline polynomial time solution for determining the optimal evacuation strategy for all agents. In the online case, where agents can communicate at a distance of two in the graph, a constant-competitive algorithm is presented. Additionally, we demonstrate that when agents are heterogeneous and each type of agent can access only a certain subgraph of the original graph, computing the optimal strategy becomes NP-hard, even with full global knowledge. This result remains true even if there are only two types of agents or, even if the optimal evacuation time is a small constant.

关键词： Discrete evacuation distributed algorithm Mobile agents Network flow Collision-free

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DER-Inverter Based Reactive Power Ancillary Service for Supporting Peer-to-Peer Transactive Energy Trading in Distribution Networks

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IEEE TRANSACTIONS ON POWER SYSTEMS 2025年第1期40卷 753-764页

作者： Zou, Yunyang Xu, Yan Nanyang Technol Univ Ctr Power Engn CPE Sch Elect & Elect Engn Singapore 639798 Singapore

Fast proliferation of distributed energy resources (DERs) and emergence of peer-to-peer (P2P) energy trading are introducing significant operation challenges to distribution networks. This paper designs a multi-timescale reactive power ancillary service trading framework in this context. The framework consists of a contract-based day-ahead Var reserve service trading stage and an hourly-ahead Var support service trading stage. At the day-ahead stage, the distribution system operator (DSO) signs a robust Var reserve contract with inverter-interfaced DERs which have sufficient Var capacity, and the problem is formulated as a two-stage robust optimization model considering uncertain nodal loads and hourly-ahead market behaviors. As an important advantage, the robust Var reserve contract eliminates the potential market power that the DERs may gain due to system configuration deficiency and market structure flaws at the later hourly-ahead trading stage. After uncertainty realization, at the hourly-ahead stage, DERs further provide the DSO with paid hourly-ahead Var supports using their spare Var capacity. The bilateral hourly-ahead Var trading between the DSO and DERs is modelled as a non-cooperative game. To attain a generalized Nash equilibrium (GNE) of the non-cooperative game, a distributed algorithm is developed by building the connection between the GNE and the primal-dual solution of a related social cost minimization problem. The proposed framework and methods are tested on three distribution systems of different scales.

关键词： Reactive power distributed power generation Costs Peer-to-peer computing Inverters Contracts Distribution networks Reactive power ancillary service DER inverter auction two-stage robust optimization non-cooperative game distributed algorithm

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Online distributed stochastic learning algorithm for convex optimization in time-varying directed networks

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NEUROCOMPUTING 2020年 416卷 85-94页

作者： Li, Jueyou Gu, Chuanye Wu, Zhiyou Chongqing Normal Univ Sch Math Sci Chongqing 400047 Peoples R China

This paper investigates a online learning optimization problem in a distributed manner, where a set of agents aim to cooperatively minimize the sum of local time-varying cost functions while communications among agents depend on a sequence of time-varying directed graphs. For such a problem, we first propose a online distributed stochastic (sub)gradient push-sum algorithm by utilizing distributed optimization methods and push-sum protocols. Then we analyze the regret bounds for the proposed algorithm on the cases when the cost functions are convex and strongly convex, respectively. The bound on the expected regret for convex functions grows sub-linearly with order of O(root T), where T is the time horizon. When the cost functions are strongly convex with Lipschitz gradients, the regret bound has an improved rate with order of O(ln T). Numerical simulations on the localization in wireless sensor networks are used to show the effectiveness of the proposed algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Online optimization Convex optimization distributed algorithm Regret bound Directed graph

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IGDT-based demand response strategy for an integrated energy system considering its interactions with multi-energy markets

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2025年 166卷

作者： Wu, Biao Zhang, Shaohua Yuan, Chenxin Wang, Xian Wang, Fei Zhang, Shengqi Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200444 Peoples R China

An integrated energy system (IES) can achieve multi-energy complementarity via its integrated demand response (IDR) program. With the continuous development of IES and the advancement of its marketization, investigating the IDR strategy of IES when it acts as a price maker in multi-energy markets holds significant importance. In this paper, we propose a bi-level model to determine the IDR strategy of an IES by considering its interactions with multi-energy markets. The upper-level formulates the IES's IDR decision-making in response to the electricity and natural gas prices, including electricity purchases from the electricity market (EM), natural gas purchases from the natural gas market (NGM), electricity and heat consumption. The lower-level describes the games of supply function bidding among the power generators (PGs) in EM and the natural gas companies (NGCs) in NGM. Specifically, using ordinal potential game (OPG) theory, we construct an ordinal potential function (OPF) for the OPG model of multi-energy markets. This enables us to find the Nash equilibrium (NE) of the games in EM and NGM through the multi-energy markets' OPF. The information gap decision theory (IGDT) is employed to address the severe uncertainty of wind power. Furthermore, the existence and uniqueness of the solution for the bi-level model are theoretically proven. Based on this, we develop a distributed algorithm to handle the information asymmetry. Simulation results demonstrate the effectiveness of the proposed model and algorithm, revealing that when IES acts as a price maker in multi-energy markets, it can mitigate the market power of both PGs and NGCs.

关键词： Integrated energy system Integrated demand response Ordinal potential game Information gap decision theory distributed algorithm

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Solving linear algebraic equations with limited computational power and network bandwidth

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SYSTEMS & CONTROL LETTERS 2025年 196卷

作者： Liu, Shenyu Martinez, Sonia Beijing Inst Technol Sch Automat Beijing 100089 Peoples R China Univ Calif San Diego Dept Mech & Aerosp Engn San Diego CA 92037 USA

This work introduces a distributed algorithm for finding least squares (LS) solutions of linear algebraic equations (LAEs). Unlike the methods studied in the literature, we assume that our distributed algorithm has limited computation power and network bandwidth, in the sense that each agent can only solve small-scale LAEs and the group of agents can only exchange messages of small size at a time. Our algorithm contains two layers of nested loops. Apart of the solution is updated by a consensus algorithm in the inner loop, while an scheduling sequence in the outer loop decides which part of the solution to be updated. By appealing to the alternating projection theorem, we prove convergence of the proposed algorithm when the scheduling sequence is both spanning and periodic. The accuracy of our algorithm is verified through a numerical example.

关键词： distributed algorithm Linear algebraic equations Least squares solutions

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Central intake optimization and decentralized decomposition for appointment scheduling and sequencing

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年第1期37卷 208-253页

作者： Seyedi, Pardis Carter, Michael W. Eshghi, Kourosh Sharif Univ Technol Dept Ind Engn Tehran Iran Univ Toronto Dept Mech & Ind Engn Toronto ON Canada

An efficient appointment scheduling system has a defining role in controlling wait times and improving the productivity of a large variety of service systems. This study addresses the variability and length of wait times. We reduce them by a form of restricted central intake. We believe it is the first study that expands the appointment scheduling-sequencing model to include multiple sites and incorporate clients' flexibility and priorities, and solves the large-size scheduling-sequencing problem in a decentralized manner. To make the study more practical, it should be compatible with the multi-stakeholder environment and consider their independency. Furthermore, the problem is large-size as it combines all requests from a geographical region into one stream. Therefore, a decentralized distributed algorithm is applied to solve the amended model. The solution approach is an ADMM-based combination of dual decomposition and augmented Lagrangian relaxation. For the application of this approach, this paper focuses on the outpatient appointment system due to its importance. Early diagnosis and prevention play a crucial role in community health and health system quality. However, patients often experience significant wait times for various diagnostic technologies worldwide. The approach is examined by a real situation of MRI in Ontario, Canada. It has been shown that this study provides better workload balance across hospitals, better responding to demand fluctuations, and alleviates excessive wait times. The computational results also show that the proposed solution method can be satisfactory in terms of accuracy, running time, and applicability. The approach developed in this study can be applicable to many practical applications of timing and sequencing, such as outpatient surgery, other diagnostic testing, home healthcare, and physical and mental therapies, as well as in other service industries beyond healthcare, like public consultations, government services.

关键词： Operations research Appointment scheduling/sequencing Healthcare management Decentralized solution distributed algorithm Service operations management Central intake

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