A method for modeling and evaluating the interoperability of multi-agent systems based on hierarchical weighted networks

doi:10.23919/JSEE.2025.000047

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (3): 754-767.doi: 10.23919/JSEE.2025.000047

• • 上一篇

收稿日期:2024-01-18 出版日期:2025-06-18 发布日期:2025-07-10

A method for modeling and evaluating the interoperability of multi-agent systems based on hierarchical weighted networks

Jingwei DONG(), Wei TANG(), Minggang YU()

Institute of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210000, China

Received:2024-01-18 Online:2025-06-18 Published:2025-07-10
Contact: Jingwei DONG E-mail:dongjingwei114@126.com;120641566@qq.com;yuminggang8989@163.com
About author:
DONG Jingwei was born in 1991. He received his B.S. degree from Information Engineering University of PLA in 2013, and M.S. degree from Amy Engineering University of PLA. He is a lecturer of Amy Engineering University of PLA. His research interest is simulation and evaluation of command and control systems. E-mail: dongjingwei114@126.com

TANG Wei was born in 1978. He received his B.S. and M.S. degrees from Amy Engineering University of PLA in 2001 and 2004, respectively. He is currently an associate professor of Army Engineering University of PLA. He research interests include command and control technology and machine learning. E-mail: 120641566@qq.com

YU Minggang was born in 1986. He received his M.S. and Ph.D. degrees from Amy Engineering University of PLA in 2012 and 2016, respectively. He is an associate professor of Army. His research interests include system of systems (SoS) engineering and theory of intelligent command and control. E-mail: yuminggang8989@163.com
Supported by:
This work was supported by the Key R&D Projects in Jiangsu Province (BE2021729), and the Key Primary Research Project of Primary Strengthening Program (KYZYJKKCJC23001).

摘要/Abstract

Abstract:

Multi-agent systems often require good interoperability in the process of completing their assigned tasks. This paper first models the static structure and dynamic behavior of multi-agent systems based on layered weighted scale-free community network and susceptible-infected-recovered (SIR) model. To solve the problem of difficulty in describing the changes in the structure and collaboration mode of the system under external factors, a two-dimensional Monte Carlo method and an improved dynamic Bayesian network are used to simulate the impact of external environmental factors on multi-agent systems. A collaborative information flow path optimization algorithm for agents under environmental factors is designed based on the Dijkstra algorithm. A method for evaluating system interoperability is designed based on simulation experiments, providing reference for the construction planning and optimization of organizational application of the system. Finally, the feasibility of the method is verified through case studies.

Key words: complex network, agent, interoperability, susceptible-infected-recovered model, dynamic Bayesian network

. [J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 754-767.

Jingwei DONG, Wei TANG, Minggang YU. A method for modeling and evaluating the interoperability of multi-agent systems based on hierarchical weighted networks[J]. Journal of Systems Engineering and Electronics, 2025, 36(3): 754-767.

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参考文献 36

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Element of the static structure of the system	Complex network model element	Formal description
Agent	Network node $ i $	$ {v}_{i} $
Capability $ A $ of agent $ i $	Attribute $ A $ of network node $ i $	$ {{A}_{v}}_{i} $
Communication relationship between agent $ i $ and agent $ j $	The edge between node $ i $ and node $ j $ on network layer $ 0 $	$ {e0}_{ij} $
Collaborative relationship $ H $ between agent $ i $ and agent $ j $	The edge between node $ i $ and node $ j $ on network layer $ H $	$ {{\mathrm{eH}}}_{ij} $
Communication capability between agent $ i $ and agent $ j $	The weight of the edges between node $ i $ and node $ j $ on network layer $ 0 $	$ {{\mathrm{W0}}}_{ij} $
Collaborative ability N between agent $ i $ and agent $ j $	The weights of the edges between node $ i $ and node $ j $ in network layer $ N $	$ {{\mathrm{WN}}}_{ij} $
Community $ k $ formed by agents	Community $ k $ formed at network layer 0	$ {{\mathrm{Cmt}}}_{k} $
Community leader L	Hub nodes in community network k	$ {{\mathrm{Cmt}}}_{kl} $

Strategy adopted in each dimension	First dimensional Monte Carlo method (selecting disturbed, damaged nodes, and edges)
Strategy adopted in each dimension	Uniform distribution	Binomial distribution	Bernoulli distribution
Continuous uniform distribution	Widely affecting all elements within the network	Randomly affecting some elements within the network	Intentionally attacking important targets within the network and having the same impact on all important targets
Normal distribution	It generally affects all elements within the network, but the degree of impact on each target varies	Randomly affecting elements within the network, but with varying degrees of impact on each target	Intentionally attacking important targets within the network, but with varying degrees of impact on each important target
Weibull distribution	All elements in the network may be paralyzed due to mechanical and electrical failures	Only a few random locations within the network will be paralyzed due to mechanical and electrical failures	The probability of mechanical and electrical failures occurring in various elements within the network varies

A method for modeling and evaluating the interoperability of multi-agent systems based on hierarchical weighted networks

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