BENI G . From swarm intelligence to swarm robotics [C ] //Proceedings of International Workshop on Swarm Robotics.Berlin, Heidelberg,Germany:Springer , 2004 : 1 - 9 .

KENNEDY J . Swarm intelligence [M ] . Handbook of Nature-inspired and Innovative Computing . Boston, MA,US:Springer , 2006 : 187 - 219 .

KARABOGA D , AKAY B . A comparative study of artificial bee colony algorithm [J ] . Applied Mathematics and Computation , 2009 , 214 ( 1 ): 108 - 132 . DOI: 10.1016/j.amc.2009.03.090 http://doi.org/10.1016/j.amc.2009.03.090 https://linkinghub.elsevier.com/retrieve/pii/S0096300309002860 https://linkinghub.elsevier.com/retrieve/pii/S0096300309002860

MARINI F , WALCZAK B . Particle swarm optimization ( PSO ). a tutorial [J ] . Chemometrics and Intelligent Laboratory Systems , 2015 , 149 : 153 - 165 . DOI: 10.1016/j.chemolab.2015.08.020 http://doi.org/10.1016/j.chemolab.2015.08.020 https://linkinghub.elsevier.com/retrieve/pii/S0169743915002117 https://linkinghub.elsevier.com/retrieve/pii/S0169743915002117

GROSS R , DORIGO M . Towards group transport by swarms of robots [J ] . International Journal of Bio-Inspired Computation , 2009 , 1 ( 1/2 ): 94 - 99 .

梁晓龙 , 张佳强 , 吕娜 . 无人机集群 [M ] . 西安 : 西北工业大学出版社 , 2018 : 1 - 6 .

LIANG X L , ZHANG J Q , LÜ N . UAV swarms [M ] . Xi'an : Northwestern Polytechnical University Press , 2018 : 1 - 6 . (in Chinese)

王华 , 赵东杰 , 杨海涛 , 等 . 大数据时代下网络群体智能研究方法 [J ] . 计算机与现代化 , 2015 ( 2 ): 1 - 6 .

WANG H , ZHAO D J , YANG H T , et al. Research method of web collective intelligence in era of big data [J ] . Computer and Modernization , 2015 ( 2 ): 1 - 6 . (in Chinese)

LI W , WU W J , WANG H M , et al . Crowd intelligence in AI2.0 era [J ] . Frontiers of Information Technology & Electronic Engineering , 2017 , 18 ( 1 ): 15 - 43 .

郭斌 . 论智能物联与未来制造——拥抱人机物融合群智计算时代 [J ] . 人民论坛学术前沿 , 2020 ( 13 ): 32 - 42 . DOI: 10. 16619/j. cnki.rmltxsqy. 2020. 13. 004 https://dx.doi.org/10. 16619/j. cnki.rmltxsqy. 2020. 13. 004 .

GUO B . On intelligent IOT and future manufacturing embracing the era of human-computer-things swarm intelligence computing [J ] . Frontiers , 2020 ( 13 ): 32 - 42 . DOI: 10. 16619/j. cnki. rmltxsqy. 2020. 13. 004 https://dx.doi.org/10. 16619/j. cnki. rmltxsqy. 2020. 13. 004 . (in Chinese)

赵健 , 张鑫褆 , 李佳明 , 等 . 群体智能2.0研究综述 [J ] . 计算机工程 , 2019 , 45 ( 12 ): 1 - 7 . DOI: 10.19678/j.issn.1000-3428.0056410 http://doi.org/10.19678/j.issn.1000-3428.0056410 在物联网、移动互联网的快速发展和大数据的深层驱动下，群体智能进入2.0时代。通过介绍群体智能2.0的定义，分析国内科研团队在群智感知计算、联邦学习及众包等方面的研究成果，明确群体智能2.0的发展趋势，并指出群智感知计算与众包技术将成为群体智能2.0的主要研究方向，数据获取、数据处理及用户隐私保护为需要解决的关键技术难点。

ZHAO J , ZHANG X T , LI J M , et al . Review on Swarm Intelligence 2.0 [J ] . Computer Engineering , 2019 , 45 ( 12 ): 1 - 7 . (in Chinese) DOI: 10.19678/j.issn.1000-3428.0056410 http://doi.org/10.19678/j.issn.1000-3428.0056410 With the rapid development of Internet of Things(IoT) as well as mobile Internet and the deep drive of big data,crowd intelligence has entered the 2.0 era.This paper introduces the definition ofcrowd intelligence 2.0,and analyzes the research results of Chinese research teams on crowd intelligence sensing and computing,federated learning and crowdsourcing.On this basis,this paper clearly indicates the development trend of crowd intelligence 2.0,where crowdsourcing technologies and crowd intelligence sensing and computing will become the focus of research,while data acquisition,data processing and user privacy protection will become the key technological challenges to be solved.

BOSKOVIC J , KNOEBEL N , MOSHTAGH N , et al . Collaborative mission planning & autonomous control technology (compact) system employing swarms of uavs [C ] //Proceedings of AIAA Guidance, Navigation, and Control Conference. Reston, VA, US:AIAA , 2009 : 5653 .

张婷婷 , 宋爱国 , 蓝羽石 . 集群无人系统自适应结构建模与预测 [J ] . 中国科学:信息科学 , 2020 , 50 ( 3 ): 16 - 19 .

ZHANG T T , SONG A G , LAN Y S . Adaptive structural modeling and prediction for unmanned swarms systems [J ] . Chinese Science: Information Science , 2020 , 50 ( 3 ): 16 - 19 . (in Chinese)

HOUSSEIN E H , SABER E , WAZERY Y M , et al . Swarm intelligence algorithms-based machine learning framework for medical diagnosis:a comprehensive review [J ] . Integrating Heuristics and Machine Learning for Real-World Optimization Problems , 2022 , 1038 : 85 - 106 .

YU M G , HE M , MA Z Y , et al. Cooperative evolution mechanism of unmanned swarm within the framework of public goods game [J ] . Mathematical Problems in Engineering , 2021 , 2021 ( 1 ): 1 - 12 .

ZHOU D Y , XUE X , ZHOU Z B . Sle2:the improved social learning evolution model of cloud manufacturing service ecosystem [J ] . IEEE Transactions on Industrial Informatics , 2022 , 18 ( 12 ): 9017 - 9026 . DOI: 10.1109/TII.2022.3173053 http://doi.org/10.1109/TII.2022.3173053 https://ieeexplore.ieee.org/document/9772408/ https://ieeexplore.ieee.org/document/9772408/

XUE X , WANG S F , ZHANG L J , et al. Social learning evolution(sle):computational experiment-based modeling framework of social manufacturing [J ] . IEEE Transactions on Industrial Informatics , 2018 , 15 ( 6 ): 3343 - 3355 . DOI: 10.1109/TII.9424 http://doi.org/10.1109/TII.9424 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=9424

XUE X , YU X N , ZHOU D Y , et al. Computational experiments for complex social systems—part iii:the docking of domain models [J ] . IEEE Transactions on Computational Social Systems , 2023 : 1 - 15 . DOI: 10.1109/TCSS.2023.3243894 https://dx.doi.org/10.1109/TCSS.2023.3243894 .

XUE X , CHEN F Y , ZHOU D Y , et al. Computational experiments for complex social systems—part i:the customization of computational model [J ] . IEEE Transactions on Computational Social Systems , 2021 , 9 ( 5 ): 1330 - 1344 . DOI: 10.1109/TCSS.2021.3125287 http://doi.org/10.1109/TCSS.2021.3125287 https://ieeexplore.ieee.org/document/9614671/ https://ieeexplore.ieee.org/document/9614671/

LU M , CHEN S Z , XUE X , et al. Computational experiments for complex social system Part Ⅱ: the evaluation of computational model [J ] . IEEE Transactions on Computational Social Systems , 2022 , 9 ( 4 ): 1224 - 1236 . DOI: 10.1109/TCSS.2021.3121323 http://doi.org/10.1109/TCSS.2021.3121323 https://ieeexplore.ieee.org/document/9604914/ https://ieeexplore.ieee.org/document/9604914/

薛霄 , 于湘凝 , 周德雨 , 等 . 计算实验方法的溯源、现状与展望 [J ] . 自动化学报 , 2023 , 49 ( 2 ): 246 - 271 .

XUE X , YU X N , ZHOU D Y , et al. Computational experiments: past, present and perspective [J ] . Acta Automatica Sinica , 2023 , 49 ( 2 ): 246 - 271 . (in Chinese)

XUE X , LI G D , ZHOU D Y , et al. Research roadmap of service ecosystems: a crowd intelligence perspective [J ] . International Journal of Crowd Science , 2022 , 6 ( 4 ): 195 - 222 . DOI: 10.26599/IJCS.2022.9100026 http://doi.org/10.26599/IJCS.2022.9100026 https://ieeexplore.ieee.org/document/9969552/ https://ieeexplore.ieee.org/document/9969552/

FROMM J . Types and forms of emergence:arXiv preprint nlin/0506028 [R ] . Ithaca,NY,US:Cornell,University , 2005 : preprint nlin/0506028 .

FALOUTSOS M , FALOUTSOS P , FALOUTSOS C . On power-law relationships of the Internet topology [J ] . Poceedings of Acm Sigcomm , 1999 , 29 ( 4 ): 251 - 262 .

VINCENT T L , BROWN J S . Evolutionary game theory, natural selection, and darwinian dynamics: evolutionary ecology [M ] //VINCENT T L , BROWN J S . New York,NY , US : Cambridge University Press , 2005 : 343 - 363 .

TAN JONATHAN H W , BOLLE F . Team competition and the public goods game [J ] . Economics Letters , 2007 , 96 ( 1 ): 133 - 139 . DOI: 10.1016/j.econlet.2006.12.031 http://doi.org/10.1016/j.econlet.2006.12.031 https://linkinghub.elsevier.com/retrieve/pii/S0165176506004447 https://linkinghub.elsevier.com/retrieve/pii/S0165176506004447

王龙 . 多智能体协调控制的演化博弈方法 [J ] . 系统科学与数学 , 2016 , 36 ( 3 ): 302 - 318 . DOI: 10.12341/jssms12735 http://doi.org/10.12341/jssms12735 提出一种基于演化博弈理论的多智能体系统协调控制方法. 在所建立的数学模型框架中智能体根据其自身的利益, 通过局部交互, 在博弈竞争中学习, 自主调整其行为. 根据系统整体性能的要求, 通过选择合适的博弈类型、设计适宜的收益计算方法、更新进化规则等, 实现对多智能体系统的控制. 在演化过程中, 无需指定某些特定个体的具体动力学行为, 只需通过种群的自适应进化即可实现整体目标. 以分工合作问题为例, 详细解析所提出的控制方法, 通过理论分析和仿真验证该方法可以实现多智能体系统的自适应协调控制.

WANG L . Evolutionary game method for multi-agent coordinated control [J ] . Systems Science and Mathematics , 2016 , 36 ( 3 ): 302 - 318 . (in Chinese)

GYORGY S , CSABA T . Evolutionary prisoner's dilemma game on a square lattice [J ] . Physical Review E , 1998 , 58 ( 1 ): 69 - 73 . DOI: 10.1103/PhysRevE.58.69 http://doi.org/10.1103/PhysRevE.58.69 https://link.aps.org/doi/10.1103/PhysRevE.58.69 https://link.aps.org/doi/10.1103/PhysRevE.58.69

ZHANG J L , ZHU Y Y , CHEN Z Q . Evolutionary game dynamics of multiagent systems on multiple community networks [J ] . IEEE Transactions on Systems, Man, and Cybernetics:Systems , 2018 , 50 ( 11 ): 4513 - 4529 . DOI: 10.1109/TSMC.6221021 http://doi.org/10.1109/TSMC.6221021 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021 https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221021

NORTH M J , TATARA E , COLLIER N T , et al . Visual agent-based model development with repast simphony [C ] //Proceedings of Agent Conference on Complex Interaction & Social Emergence.Evanston, IL , US:Department of Energy : Argonne National Laboratory , 2007 : 1 - 20 .

纪广 , 郝建国 , 张振伟 . 面向无人机作战的虚拟孪生系统设计方案 [J ] . 兵工学报 , 2022 , 43 ( 8 ): 1902 - 1912 .

JI G , HAO J G , ZHANG Z W . Design scheme of virtual twin system for UAV combat [J ] . Acta Armamentarii , 2022 , 43 ( 8 ): 1902 - 1912 . (in Chinese) DOI: 10.12382/bgxb.2021.0408 http://doi.org/10.12382/bgxb.2021.0408 To deal with the low precision of the UAV model in the process of combat simulation, the difficulty of virtualreality interactive operation, and the weak combat experience of commanders, the design scheme of the combat simulation system is proposed based on virtual twin technology, which is used for synchronous operation of actual UAV combat and simulation/deduction, and outputting intelligent assisted decision-making according to the battlefield situation. On the basis of digital twin, the connotation of virtual twin technology is proposed. Combined with the advanced means of artificial intelligence, the framework of virtual twin system for UAV combat is designed. Finally, taking the combat process of individual combat quadrotor UAV as an example, the hardware and software architecture is designed, and the operation process is analyzed. The case study findings shows that the model under the virtual twin system runs more accurately, and can realize the virtual-reality synchronization, interactive operation, visual interface and intelligent decision-making real-time output, so that the commander's sense of participation is enhanced, and the combat command efficiency is improved.