This paper investigates the Nash equilibrium seeking problem for aggregative games, where some players are under Byzantine attacks. Since the previous distributed algorithms might be seriously affected by Byzantine at...
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ISBN:
(数字)9798350382655
ISBN:
(纸本)9798350382662
This paper investigates the Nash equilibrium seeking problem for aggregative games, where some players are under Byzantine attacks. Since the previous distributed algorithms might be seriously affected by Byzantine attacks that inject malicious information to the system, we propose a resilient distributed geometric-median based method to improve the robustness of the algorithm. When less than half of the players are Byzantine attackers and under strongly monotone assumption on the pseudo-gradient mapping, we show that the algorithm converges linearly to a neighborhood of the Nash Equilibrium with an error bound related to the ratio of Byzantine attackers. Finally, numerical examples are implemented to demonstrate the robustness of the proposed algorithm under various types of Byzantine attacks.
A distributed approach is proposed in this work to solve large-scale optimization problems, called L-DATR, under the master/worker communication model. L-DATR is a distributed limited-memory trust-region method that a...
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ISBN:
(数字)9798350376647
ISBN:
(纸本)9798350376654
A distributed approach is proposed in this work to solve large-scale optimization problems, called L-DATR, under the master/worker communication model. L-DATR is a distributed limited-memory trust-region method that allows worker nodes to perform asynchronous computations. Our method dynamically adjusts the step size and direction using trust-region strategies to improve stability and convergence. To our knowledge, this is the first implementation of a distributed trust-region limited memory quasi-Newton method with robust handling of asynchronous updates and non-uniform delays between nodes. Our method is communication-efficient because it communicates only vectors of the dimension of the decision variable. Our numerical experiments match our theoretical results and showcase significant stability improvements compared to state-of-the-art distributed algorithms.
To reduce the environmental impact of port carbon emissions and promote the sustainable development of ports, this paper proposes a port distributed energy management strategy considering the charging and discharging ...
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ISBN:
(数字)9798350353334
ISBN:
(纸本)9798350353341
To reduce the environmental impact of port carbon emissions and promote the sustainable development of ports, this paper proposes a port distributed energy management strategy considering the charging and discharging of unmanned surface vehicles (USVs) under the polymorphic network. Firstly, taking into account the trend of continuous automation of ports, data centers are used to meet the ports' growing computing power needs. A data center power consumption calculation model considering data processing delay constraints is proposed. Secondly, taking the port microgrid operating cost, the main grid electricity purchase or sales cost, carbon cost, data center operating cost and USVs charging and discharging cost as objective functions, a port distributed energy management model considering USVs charging and discharging is established. Then, a distributed algorithm based on mixed integer linear programming is proposed to solve the problem. Finally, simulation results demonstrate the effectiveness of the proposed method.
We study the policy evaluation problem in multi-agent reinforcement learning, modeled by a Markov decision process. In this problem, the agents operate in a common environment under a fixed control policy, working tog...
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We study the policy evaluation problem in multi-agent reinforcement learning, modeled by a Markov decision process. In this problem, the agents operate in a common environment under a fixed control policy, working together to discover the value (global discounted accumulative reward) associated with each environmental state. Over a series of time steps, the agents act, get rewarded, update their local estimate of the value function, and then communicate with their neighbors. The local update at each agent can be interpreted as a distributed variant of the popular temporal-difference learning methods TD(lambda). Our main contribution is to provide a finite-time analysis on the performance of this distributed TD(lambda) algorithm for both constant and time-varying step sizes. The key idea in our analysis is to use the geometric mixing time tau of the underlying Markov chain, that is, although the "noise" in our algorithm is Markovian, its dependence is very weak at samples spaced out at every tau. We provide an explicit upper bound on the convergence rate of the proposed method as a function of the network topology, the discount factor, the constant lambda, and the mixing time tau. Our results also provide a mathematical explanation for observations that have appeared previously in the literature about the choice of lambda. Our upper bound illustrates the trade-off between approximation accuracy and convergence speed implicit in the choice of lambda. When lambda = 1, the solution will correspond to the best possible approximation of the value function, while choosing lambda = 0 leads to faster convergence when the noise in the algorithm has large variance.
A critical edge is an edge whose removal results in the associated undirected network becoming disconnected. Identifying these critical edges and enhancing the corresponding edge connectivity is critical for achieving...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
A critical edge is an edge whose removal results in the associated undirected network becoming disconnected. Identifying these critical edges and enhancing the corresponding edge connectivity is critical for achieving robustness in network connectivity. While existing methodologies are effective, they are centralized and rely on global information, which makes them not scalable with respect to the network size or its implementation. To address these shortcomings, a fully distributed approach is introduced in this paper to identify all the critical edges within an undirected network without requiring a central coordinating authority. Computationally, the proposed method has a complexity of ${\mathcal{O}}(n)$, where n is the number of nodes, which is more efficient when compared to the centralized approaches. Furthermore, the proposed method can be used to incrementally increase the network’s edge connectivity to 2, thus addressing the network’s most vulnerable edges.
To meet the development needs of new-type distribution networks, this paper proposes a distributed hierarchical control strategy based on honeycomb-like distribution network topology. Initially, we define the relevant...
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ISBN:
(数字)9798331505905
ISBN:
(纸本)9798331505912
To meet the development needs of new-type distribution networks, this paper proposes a distributed hierarchical control strategy based on honeycomb-like distribution network topology. Initially, we define the relevant concepts of microgrids (MGs) and establish a multi-layer control architecture based on the honeycomb-shaped distribution network. Subsequently, the paper designs methods for autonomous operation and hierarchical running of each MG area: each MG formulates day-ahead operation plans according to its own source-load characteristics; MGs achieve interaction of energy and information through base stations, while the distribution network operation center adopts the alternating direction method of multipliers (ADMM) as a distributed algorithm to realize power allocation among MGs; MGs connect with the distribution network via connection points to satisfy their energy balance. Finally, through case studies, the proposed control strategy is verified to alleviate the control burden of distribution network operators.
In this paper, we consider the problem of distributed average consensus in multi-agent systems, where each agent can come in or move out of the system, possibly multiple times. In the literature, such systems are refe...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
In this paper, we consider the problem of distributed average consensus in multi-agent systems, where each agent can come in or move out of the system, possibly multiple times. In the literature, such systems are referred to as open multi-agent systems. A typical goal in such settings is to use an iterative distributed algorithm to calculate the average of some quantities of interest each agent possesses, which can be crucial in many estimation, control, or optimization applications. We consider an open multi-agent setting and propose a distributed algorithm that allows the participating agents to track their average. More specifically, if the set of agents remaining in the computation eventually settles to a certain subset of agents, then the proposed algorithm allows them (under some mild connectivity conditions) to asymptotically reach consensus to the average of the quantities of interest these remaining agents hold. Analysis and numerical examples to illustrate the operation of the proposed algorithm are also provided.
This article proposes a distributed task allocation algorithm based on local information for unmanned aerial vehicle (UAV) swarm. A Part of the UAVs which are in the information exchange range participate in the local...
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ISBN:
(数字)9798331541729
ISBN:
(纸本)9798331541736
This article proposes a distributed task allocation algorithm based on local information for unmanned aerial vehicle (UAV) swarm. A Part of the UAVs which are in the information exchange range participate in the local range auction. The auction members will change due to the movement of the UAVs in the following auctions. Each UAV in the swarm will be allocated a task by multiple rounds of local range auctions. The experimental data clearly demonstrate that the method has superior performance for enabling UAV swarm to complete the allocation of tasks based on local information.
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorit...
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ISBN:
(数字)9798350316339
ISBN:
(纸本)9798350316346
In realistic distributed optimization scenarios, individual nodes possess only partial information and communicate over bandwidth constrained channels. For this reason, the development of efficient distributed algorithms is essential. In our paper we addresses the challenge of unconstrained distributed optimization. In our scenario each node’s local function exhibits strong convexity with Lipschitz continuous gradients. The exchange of information between nodes occurs through 3-bit bandwidth-limited channels (i.e., nodes exchange messages represented by a only 3 -bits). Our proposed algorithm respects the network’s bandwidth constraints by leveraging zoom-in and zoom-out operations to adjust quantizer parameters dynamically. We show that during our algorithm’s operation nodes are able to converge to the exact optimal solution. Furthermore, we show that our algorithm achieves a linear convergence rate to the optimal solution. We conclude the paper with simulations that highlight our algorithm’s unique characteristics.
Unmanned Aerial Vehicles (UAVs) powered by rechargeable batteries face constraints in flight duration and computational capacity. To overcome these limitations, employing multiple UAVs and efficient algorithms becomes...
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ISBN:
(数字)9798331517212
ISBN:
(纸本)9798331517229
Unmanned Aerial Vehicles (UAVs) powered by rechargeable batteries face constraints in flight duration and computational capacity. To overcome these limitations, employing multiple UAVs and efficient algorithms becomes essential for handling extensive tasks beyond the capabilities of a single UAV. Many applications, including search and rescue missions, surveillance of tall buildings, and maintenance of heritage sites, often involve diverse and dispersed positions of interest (POIs). When UAVs are stationed across multiple depots and require deployment from these locations, a UAV assignment problem is introduced, considering the heterogeneity of POIs. This assignment problem is modelled as an integer linear programming (ILP) problem aimed at minimizing the total distance travelled by all UAVs. A distributed dual ascent algorithm is proposed to solve the ILP problem efficiently.
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