Abstract
To address the lack of combat mission-driven and guided optimization methods in existing command and control (C2) system architectures, this paper proposes an optimization method for C2 system architecture based on an improved artificial rabbit optimization (ARO) algorithm, specifically the adaptive inertia weight, Levy flight, and chaotic opposite-based learning in artificial rabbit optimization (ALCARO). This method introduces a collaborative connection degree for C2 system architectures, quantitatively describing the degree of collaboration between combat units and same-level C2 units, and establishes a mathematical model for the optimization problem of C2 system architecture. The ALCARO algorithm innovatively incorporates adaptive inertia weight, Levy flight, and piecewise chaotic mapping-based opposite learning strategies to enhance the algorithm's convergence speed, effectively avoiding premature convergence to local optima and demonstrating robust performance. Simulation results show that this method can effectively enhance the connectivity among collaborative combat units in a mission-oriented manner and possesses excellent invulnerability.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
No datasets were generated or analyzed during the current study.
References
Yuan F (2023) Network centric warfare and command and control system analysis. Electron Compon Inf Technol 7(12):183–186
Zhang JR, Wang G, Wang SY (2021) Research on the architecture of air and missile defense of tactics-level command and control system. Fire Control Command Control 46(1):9–13
Zhang YL, Dai ZJ, Zhang LF et al (2020) Application of artificial intelligence in military: from projects view. In: 2020 6th International Conference on Big Data and Information Analytics (BigDIA), IEEE, pp 113–116
Schubert J, Brynielsson J, Nilsson M et al (2018) Artificial intelligence for decision support in command and control systems. In: 23rd International Command and Control Research & Technology Symposium “Multi-Domain C2”, pp 18–33
Cui J, Rao S (2021) US Army big data military applications and reflections. In: 2021 3rd International Conference on Big-data Service and Intelligent Computation, pp 92–96
Jones MA, Leon JD (2020) Multi-domain operations. Three Swords Mag 36:38–41
Magnuson S (2018) DARPA pushes ‘Mosaic Warfare’ concept. National Defense 103(780):18–19
Chen C (2018) Research on Agile C2 oriented architecture for command and control system. National University of Defense Technology
Huang DG, Zhang YX, Lin HM et al (2020) Classification model based on rule-based reasoning network. J Softw 31(4):1063–1078
Wu ZP, Xie XW (2009) Consensus convergence rate of multi-agent systems in regular networks. J Yangtze Univ (Natural Science Edition) 6(2):60–62
Erdős P, Rényi A (1959) On random graphs. Publications Mathematicae 6:290–297
Erdős P, Rényi A (1961) On the strength of connectedness of a random graph. Acta Math Hungar 12(1):261–267
Dj W (1998) Collective dynamics of ’small-world’ networks. Nature 393(6684):409–410
Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286(5439):509–512
Gilbert EN (1959) Random graphs. Ann Math Stat 30(4):1141–1144
Albert R, Jeong H, Barabási AL (2000) Error and attack tolerance of complex networks. Nature 406(6794):378–382
Crucitti P, Latora V, Marchiori M et al (2004) Error and attack tolerance of complex networks. Physica A 340(1–3):388–394
Si GY, Wang YZ, Li RJ, Wang F (2015) Modeling C2 system-of-systems based on networks. J Command Control 1(1):19–24
Li TD, Wang G, Guo XK et al (2024) Topological characteristics of air defense and antimissile system based on complex network. Firepower Command Control 49(7):30–35
Pan CS, Xiong W, Qiu SM et al (2018) Research on multi-attribute weighted command and control network modeling method. Modern Defense Technol 46(4):60–66
Hildmann H, Atia DY, Ruta D et al (2017) A model for wireless-access network topology and a PSO-based approach for its optimization. Recent Adv Comput Opt: Res Workshop Comput Opt WCO 2018:87–116
Sun CY, Shen MX, Sheng H et al (2017) Invulnerability optimization design of air defense multi-sensor network structure. J Commun 38(6):118–126
Sabino SE, Grilo AM (2022) NSGA-II based joint topology and routing optimization of flying backhaul networks. IEEE Access 10:96180–96196
Zhang Z, Wang Y, Yan MD et al (2022) Optimization of close air support super-network structure based on heuristic genetic algorithm. Ordnance Equipment Eng 43(6):121–127
Victer SR (2020) Connectivity knowledge and the degree of structural formalization: a contribution to a contingency theory of organizational capability. J Organ Des 9(2/3):929–958
Xiang Z (2022) Research on force collaboration methods in mosaic warfare based on crowdsourcing. University of Defense Technology
Sun Y, Yao PY, Wu JX et al (2016) Design method of flat command and control structure of force organization. Syst Eng Electronic Technol 38(8):1833–1839
Chen XL (2021) Research on the evaluation of command element allocation scheme for command relationship adjustment. Nanjing University of Science and Technology
Wang JW, Pan CS (2024) Research on construction method of command and control network model based on complex network theory. IET Control Theory Appl Appl. 1–13. https://doi.org/10.1049/cth2.12756.
Kong Z, Yang Q, Zhao J et al (2020) Adaptive adjustment of weights and search strategies-based whale optimization algorithm. J Northeastern Univ (Natural Science) 41(1):35
Francis B, Christophe P (2024) The statistics of Rayleigh-Levy flight extrema. Astron Astrophys 689
Long W, Jiao J, Liang X et al (2019) A random opposition-based learning grey wolf optimizer. IEEE Access 7:113810–113825
Zhou F, Liu Z, Wu L (2013) Intelligent command and control system. National Defense Industry Press, pp 74–78
Shehadeh HA (2023) Chernobyl disaster optimizer (CDO): a novel meta-heuristic method for global optimization. Neural Comput Appl 35(15):10733–10749
Dehghani M, Trojovský P (2023) Osprey optimization algorithm: a new bio-inspired metaheuristic algorithm for solving engineering optimization problems. Front Mech Eng 8:1126450
Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67
Acknowledgements
This research was supported by the National Natural Science Foundation of China Key Project: Space-Earth Integration Intelligent Network Traffic Theory and Key Technology Fund Project (61931004).
Funding
This study was funded by the National Natural Science Foundation of China, 61931004, 61931004.
Author information
Authors and Affiliations
Contributions
Jianwei Wang’s contribution is methodology, software, validation, data curation, writing—original draft, project administration, and visualization. Qing Zhang’s contribution is formal analysis, investigation, and writing—review and editing Chengsheng Pan’s contribution is conceptualization, resources, supervision, and funding acquisition.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, Jw., Zhang, Q. & Pan, Cs. Optimization method of C2 system architecture based on ALCARO. J Supercomput 81, 330 (2025). https://doi.org/10.1007/s11227-024-06768-5
Accepted:
Published:
Version of record:
DOI: https://doi.org/10.1007/s11227-024-06768-5