Distributed optimization method of directed multi-agent network based on rough information
A multi-agent, distributed technology, applied in the direction of data exchange network, digital transmission system, electrical components, etc., can solve the problem of high cost of node storage space, expand practical application scenarios, overcome excessive storage space, and reduce necessary conditional effect
Active Publication Date: 2021-03-30
东北大学秦皇岛分校
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[0007] Aiming at the deficiencies of the prior art, the present invention provides a distributed optimization method for a directed multi-agent network based on rough information, so that it no longer needs the out-degree information of the nodes, and the algorithm does not require the network Laplacian matrix Estimate the left eigenvector of the node, thus avoiding the problem of excessive cost of node storage space
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[0038] The specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0039] A distributed optimization method for directed multi-agent networks based on rough information, such as figure 1 shown, including the following steps:
[0040] Step 1: Establish a distributed optimization model for agents in a multi-agent network, specifically to solve the convex function f i The constraints of (x) and are minimized, and the properties of the convex function are set;
[0041] The constrained minimization refers to finding the convex function f when the state value x is constrained by the range i (x) and the minimum value;
[0042] The optimization model is represented by the following formula:
[0043]
[0044] where f i (x) is a strong convex function, α is its strong convex coefficient, and the constraint set is a non-empty, closed convex set, n is the number of nodes, R is the field of real numbers, and...
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The invention provides a distributed optimization method of a directed multi-agent network based on rough information, and relates to the technical field of control and information. According to the method, only rough information between adjacent nodes is considered, and each node only needs to know the state information of the in-degree neighbors, so that necessary conditions are reduced as muchas possible, and the feasibility of the algorithm in practical application is enhanced. According to the algorithm, under the condition that only rough state information between an intelligent agent and an in-degree neighbor of the intelligent agent is obtained, adjacent node information can still be well fused, node state convergence tends to be consistent, and finally an approximate optimal solution is obtained through convergence.
Description
technical field [0001] The invention relates to the field of control and information technology, in particular to a distributed optimization method of directed multi-agent network based on rough information. Background technique [0002] A multi-agent network refers to a network composed of multiple agents, each of which is a physical or abstract entity with three basic characteristics: sensing the environment, computing / processing information, and communicating with adjacent agents. In practical applications, the agents in the multi-agent network can be a set of software, or hardware such as drones and robots. According to whether global information is required, multi-agent network optimization problems can be divided into three methods: centralized, decentralized and distributed. The distributed optimization problem refers to the design of an optimization strategy for processing the global objective function by exchanging information with adjacent agents when each agent o...
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IPC IPC(8): H04L12/24H04L29/08
CPCH04L41/12H04L67/12
Inventor 陈飞金瑾项林英魏永涛孙文义
Owner 东北大学秦皇岛分校