Evolution mechanism of unmanned cluster cooperation oriented toward strategy selection diversity

doi:10.23919/JSEE.2025.000017

Journal of Systems Engineering and Electronics ›› 2025, Vol. 36 ›› Issue (2): 462-482.doi: 10.23919/JSEE.2025.000017

• SYSTEMS ENGINEERING • Previous Articles

Evolution mechanism of unmanned cluster cooperation oriented toward strategy selection diversity

Zhenhai XIE¹^,²(), Minggang YU¹(), Ming HE¹^,*(), Guoyou CHEN¹(), Zheng ZHAI¹(), Ziyu WANG¹^,²(), Lu LIU¹()

¹ Institute of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China
² Unit 31630 of the Chinese People’s Liberation Army, Huizhou 516000, China

Received:2023-01-03 Online:2025-04-18 Published:2025-05-20
Contact: Ming HE E-mail:529486527@qq.com;yuminggang8989@163.com;paper_review@126.com;549862832@qq.com;zhai-zhengreview@foxmail.com;2228370591@qq.com;305485177@qq.com
About author:
XIE Zhenhai was born in 1988. He received his B.S. degree in computer science and technology from Yangtze University, in 2009 and M.S. degree in command and control engineering from Army Engineering University of PLA in 2023. His research interest is unmanned command and control. E-mail: 529486527@qq.com

YU Minggang was born in 1986. He received his B.S. degree in the School of Information Engineering from Wuhan University of Technology in 2009, and M.S. and Ph.D. degrees from Science of Military Command, Army Engineering University of PLA in 2016. He is an associate professor of Institute of Command and Control Engineering, Army Engineering University of PLA. His research interests include game theory and complex network. E-mail: yuminggang8989@163.com

HE Ming was born in 1978. He received his B.S., M.S. and Ph.D. degrees in science of military command from Army Engineering University of PLA in 2001, 2004 and 2008, respectively. He is currently a professor of Command and Control Engineering College, Army Engineering University of PLA. His research interest is unmanned command and control. E-mail: paper_review@126.com

CHEN Guoyou was born in 1971. He received M.S. degree in science of military command from Army Engineering University of PLA in 1999. He is an associate professor of Institute of Communication Engineering, Army Engineering University of PLA. His research interests include communication engineering. E-mail: 549862832@qq.com

ZHAI Zheng was born in 1984. He received his B.S. and M.S. degrees in command and control engineering from Army Engineering University of PLA in 2006 and 2023, respectively. His research interest is UAV mission planning. E-mail: zhai-zhengreview@foxmail.com

WANG Ziyu was born in 2000. He received his B.S. degree in defense engineering from Army University of Engineering of PLA in 2022. His research interests include deep lerning and intelligent control. E-mail: 2228370591@qq.com

LIU Lu was born in 1987. He received his B.S. degree from Army Artillery Academy of PLA in 2019 and M.S. degree in communication engineering from Army Engineering University of PLA in 2024. His research interests include non war military operation training theory. E-mail: 305485177@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (72471240).

Abstract

Abstract:

When performing tasks, unmanned clusters often face a variety of strategy choices. One of the key issues in unmanned cluster tasks is the method through which to design autonomous collaboration and cooperative evolution mechanisms that allow for unmanned clusters to maximize their overall task effectiveness under the condition of strategic diversity. This paper analyzes these task requirements from three perspectives: the diversity of the decision space, information network construction, and the autonomous collaboration mechanism. Then, this paper proposes a method for solving the problem of strategy selection diversity under two network structures. Next, this paper presents a Moran-rule-based evolution dynamics model for unmanned cluster strategies and a vision-driven-mechanism-based evolution dynamics model for unmanned cluster strategy in the context of strategy selection diversity according to various unmanned cluster application scenarios. Finally, this paper provides a simulation analysis of the effects of relevant parameters such as the payoff factor and cluster size on cooperative evolution in autonomous cluster collaboration for the two types of models. On this basis, this paper presents advice for effectively addressing diverse choices in unmanned cluster tasks, thereby providing decision support for practical applications of unmanned cluster tasks.

Key words: unmanned cluster, strategy diversity, dynamic model, cooperative evolution

Zhenhai XIE, Minggang YU, Ming HE, Guoyou CHEN, Zheng ZHAI, Ziyu WANG, Lu LIU. Evolution mechanism of unmanned cluster cooperation oriented toward strategy selection diversity[J]. Journal of Systems Engineering and Electronics, 2025, 36(2): 462-482.

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Cluster cooperation	Evolutionary game
Effectiveness (capability) of unmanned platforms under specific spatial structures and strategies	Payment
Behaviors (decisions) of unmanned platforms	Strategy
Unmanned platform	Individual
Unmanned task cluster	Population
Strategy choices among unmanned platforms	Game
Proportion of individuals with different strategies in the cluster changing dynamically	Evolution
Proportion of individuals with different strategies in the cluster being stable	Evolutionarily stable
Relationships within unmanned clusters	Fully connected/ scale-free network
Unmanned platform of the study object	Individual in focus
Strategies from which unmanned platforms in a cluster can choose	Collection of cooperative strategies

Number	Parameter/variable	Meaning	Practical significance
1	$ N $	Population	Unmanned task cluster
2	$ k_{x} $	Degree of individual $ x $	Unmanned task unit’s number of neighbors
3	$ r $	Gain factor	Payoff multiplication factor of unmanned task unit during participation in cooperation
4	$ C_{x} $	Individuals participate in each cluster amount of investment	Payment by an unmanned task unit in cooperation with all of its neighboring task units (the cluster to which the neighbors belong)
5	$ f_{x} $	Payoff for individual $ x $ in the game	Cooperative payoff for an unmanned task unit in a cluster centered on its neighbors
6	$ f_{x, y} $	Game payoff for individual $ x $ in the cluster centered on individual $ y $	Payoff for task unit $ x $ after collaboration with the cluster centered on task unit $ y $, with unmanned task unit $ x $ as the focus
7	$ F_{x} $	Game payoff of individual $ x $	Specific payoff of the same task unit in multiparty, multistrategy cooperation
8	$ s_{i} $	Strategy	Cooperative behavior in which unmanned task units are willing to contribute resources to the cluster
9	$ w $	Selection intensity	Activity of the unmanned task unit in t strategy selection
10	$ \alpha $	Vision level	Expectation of the unmanned combat unit to choose strategies depending on the battlefield environment
11	$ S $	Strategy set	Number of all actions of unmanned task platforms participating in cooperative games
12	$ [a_{ij}] $	Payment value matrix	Payoff matrix generated by the cooperation between unmanned platform A adopting strategy i and unmanned platform B adopting strategy j
13	$ R $	Average cluster investment level	Overall cooperative effectiveness of unmanned cluster autonomous collaboration

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Evolution mechanism of unmanned cluster cooperation oriented toward strategy selection diversity

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