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This paper considers a class of multi-agent distributed convex optimization with a common set of constraints and provides several continuous-time neurodynamic approaches. In problem transformation, l 1 and l 2 penalty methods are used respectively to cast the linear consensus constraint into the objective function, which avoids introducing auxiliary variables and only involves information exchange among primal variables in the process of solving the problem. For nonsmooth cost functions, two differential inclusions with projection operator are proposed. Without convexity of the differential inclusions, the asymptotic behavior and convergence properties are explored. For smooth cost functions, by harnessing the smoothness of l 2 penalty function, finite- and fixed-time convergent algorithms are provided via a specifically designed average consensus estimator. Finally, several numerical examples in the multi-agent simulation environment are conducted to illustrate the effectiveness of the proposed neurodynamic approaches. [ABSTRACT FROM AUTHOR]

A distributed optimization method for solving nonlinear equations with constraints is developed in this paper. The multiple constrained nonlinear equations are converted into an optimization problem and we solve it in a distributed manner. Due to the possible presence of nonconvexity, the converted optimization problem might be a nonconvex optimization problem. To this end, we propose a multi-agent system based on an augmented Lagrangian function and prove that it converges to a locally optimal solution to an optimization problem in the presence of nonconvexity. In addition, a collaborative neurodynamic optimization method is adopted to obtain a globally optimal solution. Three numerical examples are elaborated to illustrate the effectiveness of the main results. [ABSTRACT FROM AUTHOR]

In this paper, a distributed fixed time gradient algorithm for neurodynamic systems is proposed for solving optimization problems with local inequality constraints. The algorithm is designed using fixed time theory and sliding model control techniques, where each agent has a local objective function known only to itself, and the optimal solution of each local objective function sum can be obtained in a fixed time by the information interaction between neighbors under the condition of local inequality constraints. In addition, the upper bound of the fixed time can be obtained and it is proved theoretically that the upper bound of the fixed time is independent of the initial value. Finally, the stability and effectiveness of the algorithm are verified by numerical examples. [ABSTRACT FROM AUTHOR]

In this paper, a distributed nonsmooth nonconvex optimization (DNNO) problem with affine inequality and nonsmooth convex inequality constraints is studied. A continuous-time distributed neurodynamic algorithm is proposed to solve this problem. Under the assumed conditions, for any initial state, the solution of distributed neurodynamic algorithm is bounded and globally exists, and will converge to the critical point set of distributed problems in a finite time. Compared with other DNNO algorithms, distributed neurodynamic algorithm has a lower dimension and does not need to satisfy the assumption that the feasible region is bounded. Finally, a series of numerical examples are given to verify the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a distributed algorithm that is specifically designed to tackle the least-squares problem within a network system of linear algebraic equations. Our focus is on static directed multi-agent networks, where each agent possesses knowledge of a distinct subset of the linear equations. Furthermore, we examine a scenario where agents lack information about their "out-degrees" at any given time. By imposing the strong connectivity condition on the communication network, we establish that the local estimated solution of each agent exhibits exponential convergence towards the least-squares solution of the corresponding network system of linear algebraic equations. • A consensus based distributed algorithm is devised to solve the least-squares problem. • The technique solves LS estimation over directed networks where agents lack out-degree info. • Achieves rapid exponential convergence to the least-squares solution. • Simulation confirms algorithm's theory, shows improvements over existing methods. [ABSTRACT FROM AUTHOR]

This paper studies a distributed aggregative optimization problem over a directed graph with the row-stochastic weighted matrix. Different from the existing work on distributed optimization, the local cost function of each agent depends both on its local decision variable and on the sum of all functions formed by the decision variables of all agents. Inspired by the distributed dynamic average consensus protocol, heavy-ball strategy, and Nesterov gradient descent method, a momentum-based distributed gradient tracking algorithm with a fixed step size is proposed to solve such a problem. Further, it is shown that the proposed algorithm has a linear convergence rate if the global cost function is strongly convex with the Lipschitz-continuous gradient. The upper bounds of the fixed step size and the momentum parameter are restricted by a sufficiently small positive constant, respectively. Finally, a numerical example is provided to verify the effectiveness of the findings. [ABSTRACT FROM AUTHOR]

This paper studies the problem of distributed resilient state estimation (RSE) for linear measurement models in the presence of locally-bounded Byzantine nodes that may arbitrarily deviate from the prescribed update rule. Necessary and sufficient conditions for the existence of a distributed algorithm to solve the RSE problem in the almost sure sense are characterized in term of the topology-associated robustly collective observability. Under these conditions, a distributed projected stochastic resilient filtering algorithm is proposed. Compared with the existing results where asymptotic or probabilistic finite-time analysis is established, the exponential convergence (in sense of mean square) of the proposed algorithm is proved. To further improve computational performance of the algorithm, an adaptive event-triggered mechanism is constructed without compromising its correctness of the estimate. [ABSTRACT FROM AUTHOR]

• A distributed algorithm is proposed to solve graph semi-supervised learning problem by leveraging the graph topology. • The convergence of the distributed algorithm is explicitly proved. • Numerical results verify the good performance and low cost of the distributed algorithm. Graph semi-supervised learning (GSSL) plays an important role in data classification by leveraging the similarity across the graph topology and convex optimization with Laplacian-based regularization. However, the current algorithm to solve the problem is centralized approach calling for heavy computational cost, particularly when the data is of large volume. In this paper, an innovate distributed algorithm is proposed to solve the problem, which is based on the decomposition of the similar graph. Contrary to the centralized approach, the distributed algorithm only requires the neighboring information for solving the optimization. It is proved that difference between the solutions of the distributed algorithm and centralized counterpart is upper bounded. We apply the proposed algorithm to both the synthetic and real-world datasets. The numerical results verify the effectiveness of the proposed distributed algorithm. [ABSTRACT FROM AUTHOR]

Nonconvex distributed constrained optimization over multi-agent systems plays an increasingly important role in control community. However, the duality gap for a nonconvex distributed constrained optimization problem is not always zero. This brings some difficulties for the algorithm design and convergence analysis about such problems. In this paper, to eliminate the duality gap, the original distributed optimization problem is converted to an equivalent distributed one by using a partial power reformulation. Next, for solving this equivalence problem, a continuous-time algorithm is proposed over the multi-agent system where each agent exchanges local information with other agents under an undirected communication graph. It is proved that the proposed continuous-time algorithm locally converges to a strict local optimal solution of the considered original nonconvex distributed optimization problem. Numerical examples show the validness of the presented algorithm. Finally, the algorithm is applied for optimal placement. [ABSTRACT FROM AUTHOR]

This paper analyzed the temporary overvoltage (TOV) problem in the network distribution system. The TOV shows different patterns depending on the type of distributed energy resources (DERs) and the grounding method. In particular, the inverter-based distributed generations (IBDGs) also show different patterns as inverter's dynamic characteristics depending on the classification of grid forming (GFM) and grid following (GFL). The paper reviewed existing factors affecting TOV according to the inverter type. In addition, the TOV was simulated in various topologies of distribution systems, indicating that distribution system topology can solve the TOV problem regardless of the inverter type. This paper compared the TOV in the radial distribution system and the networked distribution system (NDS), and as a result, the operation through NDS can be a solution to the TOV problem. In addition, TOV in the NDS is analyzed through simulation, considering various elements of the distribution system to prove a more robust solution. [ABSTRACT FROM AUTHOR]

The graph exploration problem requires a group of mobile robots, initially placed arbitrarily on the nodes of a graph, to work collaboratively to explore the graph such that each node is eventually visited by at least one robot. One important requirement of exploration is the termination condition, i.e., the robots must know that exploration is completed. The problem of live exploration of a dynamic ring using mobile robots was recently introduced by Di Luna et al. [DC 2020]. More specifically, they studied the problem of live exploration in a dynamic ring and proposed multiple algorithms to solve this problem in the fully synchronous and semi-synchronous settings with various guarantees when 2 robots were involved. They also showed that, under certain assumptions, exploration of the ring using two robots was impossible. An important question left open was how the presence of 3 robots would affect the results. In this paper, we try to settle this question in a fully synchronous setting and also show how to extend our results to a semi-synchronous setting. In particular, we present algorithms for exploration with explicit termination using 3 robots in conjunction with either (i) unique IDs of the robots and edge crossing detection capability (i.e., two robots moving in opposite directions through an edge in the same round can detect each other), or (ii) access to randomness. The time complexity of our deterministic algorithm is asymptotically optimal. We also provide complementary impossibility results showing that there does not exist any explicit termination algorithm for 2 robots even when each robot has a unique ID, edge crossing detection capability, and access to randomness. The theoretical analysis and comprehensive simulations of our algorithm show the effectiveness and efficiency of the algorithm in dynamic rings. We also present an algorithm to achieve exploration with partial termination using 3 robots with unique IDs in the semi-synchronous setting, when robots have access to edge crossing detection capability and randomness but do not know a bound on the size of the ring or have access to a landmark or are guaranteed that robots have common chirality. Our algorithms are fully decentralized, lightweight, and easily implementable. [ABSTRACT FROM AUTHOR]

As the scale of distributed control systems over networks increases and interactions between different subsystems become more sophisticated, questions of the resilience of such networks to attacks and faults increase in importance. The need to adapt (and redefine) classical system and control-theoretic notions to answer such questions using the language of graphs has recently started to gain attention as a fertile and important area of research. This paper presents an overview of graph-theoretic methods for analyzing the resilience of distributed control systems. We survey distributed algorithms that have been proposed to solve a variety of problems in networked systems, and summarize their resilience against adversarial actions in the context of the structure of the underlying networks. We also discuss graph-theoretic methods to quantify the attack impact, and reinterpret some system-theoretic notions of robustness from a graph-theoretic standpoint to mitigate the impact of the attacks. We conclude by introducing some avenues for further research in this field. [ABSTRACT FROM AUTHOR]

Multi-agent reinforcement learning (MARL) has been successfully applied in many fields. In MARL, the policy evaluation problem is one of crucial problems. In order to solve this problem, distributed Temporal-Difference (TD) learning algorithm is one of the most popular methods in a cooperative manner. Despite its empirical success, however, the theory of the adaptive variant of distributed TD learning still remain limited. To fill this gap, we propose an adaptive distributed temporal-difference algorithm (referred to as MS - ADTD) under Markovian sampling over time-varying networks. Furthermore, we rigorously analyze the convergence of MS - ADTD , the theoretical results show that the local estimation can converge linearly to the optimal neighborhood. Meanwhile, the theoretical results are verified by simulation experiments. • This paper proposes a distributed adaptive TD learning algorithm under Markovian sampling. • It theoretically analyzes the non-asymptotic convergence performance of the proposed algorithm. • It verifies the performance of the proposed algorithm by experiments. [ABSTRACT FROM AUTHOR]

This paper tackles an estimation problem in networked systems. A given time-varying signal is assumed to be measured or computed by only a subset of agents, named sources , while the other agents, the users , are required to estimate this signal in a distributed way. Each agent communicates only with a subset of neighbouring mates and the communication topology is described by a directed graph with relatively weak connectivity properties. The problem is solved for two class of signals with, respectively, null or bounded derivative of a given order, by resorting to a bank of distributed estimators of the signal and its derivatives run by each user agent. Convergence properties and noise rejection capabilities are investigated. Simulations are run to show the effectiveness of the approach and assess its performance. [ABSTRACT FROM AUTHOR]

This paper considers the problem of solving distributed online optimization over a network that consists of multiple interacting nodes. Each node in the network is endowed with a sequence of loss functions, each of which is revealed to the node after a decision has been committed. The goal of the network is to minimize the cumulative loss functions of nodes in a distributed fashion, while subject to two types of information constraints, namely, message quantization and bandit feedback. To this end, a quantized distributed online bandit optimization algorithm is proposed by adopting random quantization operation and one-point gradient estimator. We show the convergence of the algorithm by establishing an O (d T 3 / 4) regret bound, where d is the dimension of states and T is the total number of rounds. Finally, an online distributed quadratic programming problem is investigated to validate the theoretical findings of the paper. [ABSTRACT FROM AUTHOR]

In this paper we consider the problem of reducing control costs in Multi-Agent Systems in a distributed fashion, while maintaining or seeking a certain level of global performance. The problem is formulated to minimize the controller gains locally, while respecting a global bound on the state deviations due to effects of external disturbances We show that with the chosen performance measure this can be reformulated as a convex optimization problem. However, due to the limited knowledge of the agents and structural changes in the networks, currently existing algorithms for solving distributed optimization problems cannot be used as is. We therefore derive an algorithm that is capable of solving the problem, while also respecting limitations caused by the aforementioned issues. We end the paper with an example where the problem is solved in a Multi-Terminal DC-grid. [ABSTRACT FROM AUTHOR]

Vehicular edge computing (VEC) pushes the computational resources to the logical edge of the networks, thus enabling vehicles to run resource-hungry and time-sensitive applications by outsourcing operations. Many studies revolved around VEC focus on the optimization of response latency, energy consumption, or both of them, assuming that the computational resources in VEC can be utilized for free. However, VEC provisions computational resources on a pay-as-you-go basis, which means VEC can obtain revenues by leasing the computational resources. In this paper, we focus on the real-time computational resource pricing in VEC, in the hope to reach a win–win situation where both VEC and vehicles can optimize their respective utility values. To reach such a mutually satisfactory result, we adopt the Stackelberg game to model the computational resource pricing problem in this paper. In this game, vehicles are followers and the edge server serves as the leader. Furthermore, we have proven that a unique Stackelberg equilibrium exists in the proposed pricing game. A distributed algorithm is put forward to solve our problem, which considers the privacy of vehicles. The distributed approach is evaluated by extensive experiments, in terms of convergence rate, running time and so on. The simulation results demonstrate that the distributed approach can achieve satisfactory results without privacy disclosure compared to the centralized approach. [ABSTRACT FROM AUTHOR]

• The data-driven inverse optimization corrects the DR execution online. • The proposed strategy is realized in a distributed manner and free of initialization. • This paper provides rigorous stability proof for the proposed optimization. • D-PPDS algorithm solves the DR optimization with global inequality constrain. This paper provides an insight into the demand response (DR) optimization in distribution markets consisting of a retailer and multiple demand response aggregators (DRA), where a retailer determines DR incentives based on power consumption profile. Conventional DR optimization with global states and constraints is intractable to be implemented in a distributed framework, which restricts the application feasibility and the potential profit of DR. To handle these limitations, we design a multi-agent architecture for distributed demand response (DDR). An online data-mining method is developed to identify the characteristics of DR. A leader–follower structure decomposes the original problem into a leader problem with global variables and aggregators of sub-problems, where discrete singular consensus is designed to broadcast the leader's strategy to followers in real-time. The distributed perturbation primal–dual sub-gradient (D-PPDS) algorithm is proposed to solve the DDR problem with global inequality constraints in a completely distributed fashion. The proposed DDR strategy is tested by an actual case. The simulation results demonstrate that the asynchronous D-PPDS algorithm can obtain the near-optimal solution of the problem with global inequality constraints, and is robust against delay or plug-and-play. [ABSTRACT FROM AUTHOR]

Pool Model is an asynchronous, loosely coupled distributed evolutionary algorithm (dEA) design architecture. However, the classical Pool Model face some design problems, such as population control, work redundancy, rough selection/replacement strategies, and unreliable connections, etc. In this paper, a novel distributed pool evolutionary algorithm (EA) model with buffer (PEAB) is proposed. PEAB can solve the inherent problems of the Pool Model by using the buffer setting, the Reunion mechanism, and the Migration in Pool (MP) strategy. Besides, PEAB provides stronger population control and more global population selection/replacement strategies. In the experimental part, we compared PEAB with another Pool Model named EvoSpace using a common benchmark. The experiments showed that the convergence rate of PEAB is 59.7% faster than that of EvoSpace under the respective fastest conditions. PEAB also has a faster reception rate of the first generation and stronger population control. Besides, this paper also tests and analyzes the scalability of PEAB using two other benchmarks. The overall trend of the experiment results suggested that PEAB would be faster with more Workers. Last but not least, this paper studies the effect of the MP strategy on the performance of PEAB, and the results showed that the MP strategy can effectively improve the convergence efficiency. • A novel Pool-based Distributed Evolutionary Algorithm Model PEAB is proposed. • The Buffer Zone prevents the loss of good individuals and work redundancy. • The Reunion mechanism can improve the quality of the EA. • The Migration in Pool strategy can accelerate the convergence of the EA. [ABSTRACT FROM AUTHOR]

To achieve large-scale generalized demand-side resources to participate in the "peak-shaving and valley-filling" of the power system, a distributed cooperative control strategy for discrete-time linear multi-agents considering historical data is proposed, which comprehensive consider the changes in load commands, topology, multi-objective function coefficients, communication delays and input delays. This paper combines the distributed multi-agent system with the hierarchical architecture to solve the problems of extensive data calculations in centralized control, which may easily cause communication channel blockage and delay. Considering the cost of changing the generalized demand-side resources and the user's comfort, the pre-learning historical data storage strategy based on K-means clustering is adopted before the actual task, and the idea of transfer learning is introduced into the consensus algorithm. Finally, through experimental statistics, the number of iteration steps of the consensus algorithm is reduced and the effectiveness of the proposed algorithm is proved. [ABSTRACT FROM AUTHOR]

In this paper, the problem of distributively solving mixed equilibrium problems is studied by employing a multi-agent network. The goal of agents is to cooperatively find a point in a convex set such that the sum of multiple bifunctions with a free variable is non-negative. Different from the existing works, here we consider the case where bifunctions do not possess the cut property. To solve this problem, we propose a distributed ergodic algorithm based on the ergodic algorithm and consensus algorithm. When running the proposed algorithm, each agent makes decisions only using its own bifunction information and the local state information received from its immediate neighbors. Under mild conditions on bifunctions and the graph, we prove that outputs of all agents weakly converge to a common solution of the mixed equilibrium problem. Finally, a numerical simulation example is worked out to demonstrate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

With ridesourcing services gaining popularity in the past few years, there has been growing interest in algorithms that could enable real-time operation of these systems. As ridesourcing systems rely on independent entities to build the supply and demand sides of the market, they have been shown to operate more successfully in metropolitan areas where there is a high level of demand for rides as well as a high number of drivers, and a large volume of trips occurring within a geographically constrained region. Despite the suitable ecosystem that metropolitan areas offer for ridesourcing operations, there is a lack of methods that can provide high-quality matching solutions in real-time. To fill this gap, this paper introduces a framework that allows for solving the large-scale matching problems by means of solving smaller problems in a distributed fashion. The proposed methodology is based on constructing approximately-uniform clusters of trip requests, where vehicle tours form cluster centers. Using the New York Taxi dataset, we compare the performance of the proposed methodology against three benchmark methods to showcase its advantages in terms of solution quality and solution time. • A distributed algorithm to solve the ride-matching problem. • A methodology based on forming approximately-uniform clusters of rides. • Cluster representatives are in form of vehicle tours. • Higher solution quality compared to the state-of-the-art partitioning methods. [ABSTRACT FROM AUTHOR]

• Calculation speed, information privacy, and multiple objectives are considered. • Layering, multi-objective economic dispatch and distributed algorithm are combined. • A framework of multi-layer distributed multi-objective economic dispatch is proposed. • The multi-layer distributed multi-objective consensus algorithm is proposed. • The proposed algorithm has higher information privacy and faster calculation speed. Traditional multi-objective economic dispatch problems all apply centralized multi-objective optimization methods. However, with the rapid development of smart grids, issues such as system calculation speed, robustness, and information privacy have been increasingly vitally. Although the distributed multi-objective optimization method can solve the problems of system robustness and privacy when the scale of the interconnected power system expands and the number of agents increases, each agent independently optimizes its sub-problems and then exchanges information to complete the entire system optimization. The pressure of system communication calculation leads to reduce the overall calculation speed greatly. In response to the above problems, this paper proposes a multi-layer distributed multi-objective consensus algorithm. This method first calculates the optimal power generation of each area of each layer through the network topology and then calculates the power of each unit in each area in parallel according to the calculated optimal power generation. The analysis of the three system simulation results of IEEE118-bus, IEEE2154-bus, and Xinjiang Turpan 117-bus shows that the proposed method can solve the problems of multi-objective and information privacy, and realize the rapid solution of economic dispatch in large-scale multi-area interconnected power systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel control and planning algorithm for cooperative slung-load transportation using multiple quadrotors. Each aerial robot modeled as a quadrotor is connected to a non-uniform payload by the cables. Udwadia-Kalaba equations are used for calculating the forces on the cables acting on each robot. A novel leaderless distributed adaptive control algorithm is presented which not only ensures the safe transport of payload in the generated path in the presence of external disturbances but also manages to solve the problems related to the interaction of payload with the aerial robots. Moreover, the payload dynamics is not considered in the design of the controller which facilitates the load transportation in any size, shape, or weight by the robotic team without compromising performance. Then, an improved rapidly exploring random tree star (RRT*) algorithm is developed to generate desired path for each aerial robot in order to avoid known obstacles for cooperative payload transportation problem. The proposed path planning algorithm is capable of finding a free collision path in any situation, specially where high maneuverability is crucial to avoid collision of the payload with obstacles. Three numerical simulations are conducted to verify the proposed planning and control scheme. Additionally, in the distributed control algorithm simulation, two indexes including ISE and IADU demonstrate the improved performance of the distributed control over independent control of robots. The superior performance of the proposed controller in terms of reduced tracking errors in presence of external disturbances is also shown in comparison with a distributed nonlinear H ∞ design. [ABSTRACT FROM AUTHOR]

This paper develops a distributed approach for inverse optimal control (IOC) in multi-agent systems. Here each agent can only communicate with certain nearby neighbors and only accesses segments of system's trajectory, which is not sufficient for the agent to solve the IOC problem alone. By introducing the concept of the data effectiveness and bridging the connection between each segment and its contribution to solving IOC, we formulate the IOC problem as a problem of achieving least-square solutions via a distributed algorithm. Simulations are provided to validate the proposed distributed IOC approach. [ABSTRACT FROM AUTHOR]