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

doi:10.23919/JSEE.2025.000047

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

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.

Figures/Tables 13

Fig 1

Table 1

Fig 2

Fig 3

Fig 4

Fig 5

Fig 6

Table 2

Fig 7

Fig 8

Fig 9

Fig 10

Fig 11

References 36

1	KAI P, KHURUM M, ANGELIS L Reasons for bottlenecks in very large-scale system of systems development. Information and Software Technology, 2014, 56 (10): 1403- 1420.
2	AMRANI A E N, ABRA K E O, YOUSSFI M Deep learning approach for semantic interoperability between heteregeneous multi-agent systems. Engineering, Technology and Applied Science Research, 2019, 9 (4): 4566- 4573.
3	WANG L, HUANG F Multi agent game, learning and control. Journal of Automation, 2023, 49 (3): 580- 613.
4	YANG J, LIANG Y G, LI K, et al Architecture design of unmanned aerial vehicle (UAV) cluster collaborative combat based on SysML. Proc. of the 5th Academic Conference on System Engineering, 2023, 49- 55.
5	YU M G, HE M, MA Z Y, et al Cooperative evolution mechanism of unmanned swarms within the framework of public goods game. Mathematical Problems in Engineering, 2021, 2021 (1): 5575815.
6	XU Y M, ZHANG Y C, ZHANG J Q, et al A study on the network model and timeliness of forest fire emergency rescue process based on petri nets. Disaster Science, 2024, 39 (2): 42- 47.
7	ZHAO Y L, JI C, JIANG F X, et al Research on interoperability testing of IS-IS routing protocol based on colored petri nets. Computer Engineering and Science, 2013, 35 (12): 90- 95.
8	ENOS R J, NILCHIANI R R Understanding the importance of expanding the definition of interoperability through social network analysis. Systems Engineering, 2020, 23 (2): 139- 153.
9	DU W, CHENG X S, YANG C, et al Establishing interoperability among knowledge organization systems for research management: a social network approach. Scientometrics, 2017, 112 (3): 1489- 1506.
10	YU M G, CHEN J, HE M, et al Collaborative mechanism of unmanned clusters in community networks based on evolutionary games. Chinese Science: Technical Science, 2023, 53 (2): 221- 242.
11	LIU Z X, WANG W J, PAN L Research on the durability of marine freight networks based on multi-layer complex network theory. Ocean Bulletin, 2018, 37 (6): 652- 658.
12	JIA Y Q, SONG C H, PENG L, et al. Modeling and feature analysis of air traffic management technical support system based on weighted complex network. Proc. of the 2nd International Conference on Machine Learning and Machine Intelligence, 2019, 73−78.
13	JIANG Q Y, LIN L, LING L L, et al Dynamical analysis of a three-dimensional discrete-time SIR model. Journal of Difference Equations and Applications, 2024, 30 (6): 721- 754.
14	PUNYA A, MCCAW J M, TAYLOR G P Improving estimates of waning immunity rates in stochastic SIRS models with a hierarchical framework. Infectious Disease Modelling, 2023, 8 (4): 1127- 1137.
15	JIANG J Y, JIA W S, JIE W, et al Modelling network public opinion polarization based on SIR model considering dynamic network structure. Alexandria Engineering Journal, 2022, 61 (6): 4557- 4571.
16	SIBAJI R, JANA S, ANUPAM K, et al Complex dynamics of a Caputo derivative-based fractional-order SIR model incorporating saturated incidence and recovery. International Journal of Dynamics and Control, 2023, 12 (1): 246- 258.
17	CUI Y G, HANS S D, GILBERT A D, et al Multilevel Monte Carlo methods for stochastic convection-diffusion eigenvalue problems. Journal of Scientific Computing, 2024, 99 (3): 77.
18	WU X, JIANG J Q, WU T Y, et al A Monte Carlo method for quantitatively calculating the neutron sensitivity of rhodium self-powered neutron detectors in reactors. Radiation Detection Technology and Methods, 2023, 8 (2): 1162- 1170.
19	MIGOV D A, WEINS D V On network reliability evaluation by Monte Carlo method using high-performance computing. Lobachevskii Journal of Mathematics, 2023, 44 (8): 3122- 3129.
20	VEYSI B, MELIH Y, GUL M, et al A fuzzy Bayesian network risk assessment model for analyzing the causes of slow-down processes in two-stroke ship main engines. Ships and Offshore Structures, 2024, 19 (5): 670- 686.
21	HAO Y M, NU W X, XIANG L, et al Analysis of rockburst mechanism and warning based on microseismic moment tensors and dynamic Bayesian networks. Journal of Rock Mechanics and Geotechnical Engineering, 2023, 15 (10): 2521- 2538.
22	JI X Z, MING H S, XIAO S L, et al Dynamic risk evaluation of hydrogen station leakage based on fuzzy dynamic Bayesian network. International Journal of Hydrogen Energy, 2024, 50 (10): 1131- 1145.
23	JIAN G, KAI J M Risk analysis for hazardous chemical vehicle-bridge transportation system: a dynamic Bayesian network model incorporating vehicle dynamics. Reliability Engineering and System Safety, 2024, 242 (1): 109- 129.
24	QIAN L, LE Q, QIAN L S, et al Optimization of the active battery immersion cooling based on a self-organized fluid flow design. Journal of Energy Storage, 2024, 76 (11): 284- 299.
25	ZE Z L, HONG C Z A novel fractional uplink power control framework for self-organizing network. Digital Communications and Networks, 2023, 9 (6): 1434- 1440.
26	KAYO F, JACKY K, GUPPY S, et al Beyond scale-free networks: integrating multilayer social networks with molecular clusters in the local spread of COVID-19. Scientific Reports, 2023, 13 (1): 1861- 1865.
27	QING Y Z, PING F X, KE F C, et al Hybrid propagation and control of network viruses on scale-free networks. Bulletin of the Iranian Mathematical Society, 2023, 49 (6): 54- 87.
28	MOSTAK A, BIN A M, ALAM M S Bifurcation analysis and optimal control of discrete SIR model for COVID-19. Chaos, Solitons and Fractals: the Interdisciplinary Journal of Nonlinear Science, and Nonequilibrium and Complex Phenomena, 2023, 174 (9): 451- 468.
29	LI D L, YI Z, YANG D Rumor spreading model with a focus on educational impact and optimal control. Nonlinear Dynamics, 2023, 112 (2): 1575- 1597.
30	YU M G, NIE Y J, LI X D, et al Adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game. Journal of Systems Engineering and Electronics, 2023, 34 (3): 598- 614.
31	YU M G, HE M, ZHANG D G, et al Dominant conditions of unmanned combat swarm strategies based on evolutionary game of multiple public goods. Journal of Systems Engineering and Electronics, 2021, 43 (9): 2553- 2561.
32	KUMAR P R, MOHAN B G, ELAKKIYA R, et al Retroactive data structure for protein-protein interaction in lung cancer using Dijkstra algorithm. International Journal of Information Technology, 2023, 16 (2): 1239- 1251.
33	LEAL G, WIDED G, HERVE P Enterprise interoperability assessment: a requirements engineering approach. International Journal of Computer Integrated Manufacturing, 2020, 33 (3): 265- 286. doi: 10.1080/0951192X.2020.1736636
34	ZHANG X S, SU X, WANG Y M Research on evaluation methods for interoperability of military information systems. Journal of China Academy of Electronic Science, 2016, 11 (6): 649- 654.
35	WANG L S, LING S W Understandable capacity level assessment method for networking information-centric system-of-systems interoperability. Journal of Physics: Conference Series, 2020, 1584 (1): 12- 46.
36	AN X M, QI Y Research on the framework of data interoperability capability guarantee elements for public data platforms. Intelligence Theory and Practice, 2024, 47 (1): 46- 56.

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

[1]	Xueqiang GU, Lina LU, Fengtao XIANG, Wanpeng ZHANG. Formation-containment control for nonholonomic multi-agent systems with a desired trajectory constraint [J]. Journal of Systems Engineering and Electronics, 2025, 36(1): 256-268.
[2]	Jun LIU, Xiaolong LIANG, Pengfei LEI. Capacity allocation strategy against cascading failure of complex network [J]. Journal of Systems Engineering and Electronics, 2024, 35(6): 1507-1515.
[3]	Nanxun DUO, Qinzhao WANG, Qiang LYU, Wei WANG. Tactical reward shaping for large-scale combat by multi-agent reinforcement learning [J]. Journal of Systems Engineering and Electronics, 2024, 35(6): 1516-1529.
[4]	Tao WANG, Zhi ZHU, Xin ZHOU, Tian JING, Wei CHEN. A function-based behavioral modeling method for air combat simulation [J]. Journal of Systems Engineering and Electronics, 2024, 35(4): 945-954.
[5]	Jia ZHANG, Xin DU, Qichen DONG, Bin XIN. Distributed collaborative complete coverage path planning based on hybrid strategy [J]. Journal of Systems Engineering and Electronics, 2024, 35(2): 463-472.
[6]	Ruihan ZHANG, Bing SUN. Complex adaptive system theory, agent-based modeling, and simulation in dominant technology formation [J]. Journal of Systems Engineering and Electronics, 2024, 35(1): 130-153.
[7]	Donghao QIN, Le WANG, Jiuan GAO, Jianxiang XI. Minimum-energy leader-following formation of distributed multi-agent systems with communication constraints [J]. Journal of Systems Engineering and Electronics, 2023, 34(6): 1419-1431.
[8]	Shunqi YANG, Ying ZENG, Xiang LI, Yanfeng LI, Hongzhong HUANG. Reliability analysis for wireless communication networks via dynamic Bayesian network [J]. Journal of Systems Engineering and Electronics, 2023, 34(5): 1368-1374.
[9]	Kewei YANG, Jichao LI, Maidi LIU, Tianyang LEI, Xueming XU, Hongqian WU, Jiaping CAO, Gaoxin QI. Complex systems and network science: a survey [J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 543-573.
[10]	Yimin FENG, Chenchu ZHOU, Qiang ZOU, Yusheng LIU, Jiyuan LYU, Xinfeng WU. A goal-based approach for modeling and simulation of different types of system-of-systems [J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 627-640.
[11]	Zhengyu YE, Bin JIANG, Yuehua CHENG, Ziquan YU, Yang YANG. Distributed fault diagnosis observer for multi-agent system against actuator and sensor faults [J]. Journal of Systems Engineering and Electronics, 2023, 34(3): 766-774.
[12]	Pu YANG, Xukai HU, Zixin WANG, Zhiqing ZHANG. Sliding mode fault tolerant consensus control for multi-agent systems based on super-twisting observer [J]. Journal of Systems Engineering and Electronics, 2022, 33(6): 1309-1319.
[13]	Fang YE, Ying MAO, Yibing LI, Xinrui LIU. Target threat estimation based on discrete dynamic Bayesian networks with small samples [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1135-1142.
[14]	Ang GAO, Qisheng GUO, Zhiming DONG, Zaijiang TANG, Ziwei ZHANG, Qiqi FENG. Research on virtual entity decision model for LVC tactical confrontation of army units [J]. Journal of Systems Engineering and Electronics, 2022, 33(5): 1249-1267.
[15]	Wenzhang LIU, Lu DONG, Jian LIU, Changyin SUN. Knowledge transfer in multi-agent reinforcement learning with incremental number of agents [J]. Journal of Systems Engineering and Electronics, 2022, 33(2): 447-460.