scholarly journals Distributed Newton Methods for Strictly Convex Consensus Optimization Problems in Multi-Agent Networks

Symmetry ◽  
2017 ◽  
Vol 9 (8) ◽  
pp. 163 ◽  
Author(s):  
Dong Wang ◽  
Hualing Ren ◽  
Fubo Shao
Keyword(s):  
Optimization Problems ◽  
Newton Methods ◽  
Strictly Convex ◽  
Multi Agent ◽  
Consensus Optimization ◽  
Agent Networks

A randomized incremental primal-dual method for decentralized consensus optimization

2019 ◽  
pp. 1-25
Author(s):  
Chenxi Chen ◽  
Yunmei Chen ◽  
Xiaojing Ye
Keyword(s):  
Convergence Rates ◽  
Optimization Problems ◽  
Dual Method ◽  
Selection Strategy ◽  
Consensus Algorithms ◽  
Uniform Sampling ◽  
Primal Dual ◽  
Multi Agent ◽  
Dual Variables ◽  
Consensus Optimization

We consider a class of convex decentralized consensus optimization problems over connected multi-agent networks. Each agent in the network holds its local objective function privately, and can only communicate with its directly connected agents during the computation to find the minimizer of the sum of all objective functions. We propose a randomized incremental primal-dual method to solve this problem, where the dual variable over the network in each iteration is only updated at a randomly selected node, whereas the dual variables elsewhere remain the same as in the previous iteration. Thus, the communication only occurs in the neighborhood of the selected node in each iteration and hence can greatly reduce the chance of communication delay and failure in the standard fully synchronized consensus algorithms. We provide comprehensive convergence analysis including convergence rates of the primal residual and consensus error of the proposed algorithm, and conduct numerical experiments to show its performance using both uniform sampling and important sampling as node selection strategy.

New methods to realize the cluster consensus for multi‐agent networks

2019 ◽  
Vol 22 (6) ◽  
pp. 2549-2557
Author(s):  
Xing Guo ◽  
Jinling Liang ◽  
Habib M. Fardoun
Keyword(s):  
New Methods ◽  
Multi Agent ◽  
Cluster Consensus ◽  
Agent Networks

scholarly journals Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context

Games ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Chica-Pedraza ◽  
Eduardo Mojica-Nava ◽  
Ernesto Cadena-Muñoz
Keyword(s):  
Control Method ◽  
Optimization Problems ◽  
Replicator Dynamics ◽  
Boltzmann Distribution ◽  
Full Information ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Population Games ◽  
Distributed Approach ◽  
Multi Agent

Multi-Agent Systems (MAS) have been used to solve several optimization problems in control systems. MAS allow understanding the interactions between agents and the complexity of the system, thus generating functional models that are closer to reality. However, these approaches assume that information between agents is always available, which means the employment of a full-information model. Some tendencies have been growing in importance to tackle scenarios where information constraints are relevant issues. In this sense, game theory approaches appear as a useful technique that use a strategy concept to analyze the interactions of the agents and achieve the maximization of agent outcomes. In this paper, we propose a distributed control method of learning that allows analyzing the effect of the exploration concept in MAS. The dynamics obtained use Q-learning from reinforcement learning as a way to include the concept of exploration into the classic exploration-less Replicator Dynamics equation. Then, the Boltzmann distribution is used to introduce the Boltzmann-Based Distributed Replicator Dynamics as a tool for controlling agents behaviors. This distributed approach can be used in several engineering applications, where communications constraints between agents are considered. The behavior of the proposed method is analyzed using a smart grid application for validation purposes. Results show that despite the lack of full information of the system, by controlling some parameters of the method, it has similar behavior to the traditional centralized approaches.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

Distributed Online Gradient Boosting on Data Stream over Multi-agent Networks

2021 ◽  
pp. 108253
Author(s):  
Xibin An ◽  
Chen Hu ◽  
Gang Liu ◽  
Haoshen Lin
Keyword(s):  
Data Stream ◽  
Gradient Boosting ◽  
Multi Agent ◽  
Agent Networks
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