IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v366y2020ics0096300319306289.html
   My bibliography  Save this article

Robustness of multi-agent formation based on natural connectivity

Author

Listed:
  • Deng, ZhengHong
  • Xu, Jiwei
  • Song, Qun
  • Hu, Bin
  • Wu, Tao
  • Huang, Panfei

Abstract

The robustness of multi-agent topology has great importance to the design of multi-agent formation, while to improve the robustness of the topology without increasing its cost is also very important. In this paper, we proposed an optimization method for the robustness of multi-agent formation. The natural connectivity is used for measuring the robustness of multi-agent formation. Through analysis, the formation optimization problem is transformed into a 0–1 nonlinear programming problem. In order to solve the problem quickly, the paper presents a genetic algorithm based on chaotic search optimization. When the method given in this paper is applied to specific requirements, only the constraint conditions need to be modified, so the method has good universality. The results show that the optimized network can significantly improve the robustness of the network.

Suggested Citation

  • Deng, ZhengHong & Xu, Jiwei & Song, Qun & Hu, Bin & Wu, Tao & Huang, Panfei, 2020. "Robustness of multi-agent formation based on natural connectivity," Applied Mathematics and Computation, Elsevier, vol. 366(C).
    • Handle: RePEc:eee:apmaco:v:366:y:2020:i:c:s0096300319306289
    DOI: 10.1016/j.amc.2019.124636
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300319306289
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2019.124636?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xiao, Yang-Hua & Wu, Wen-Tao & Wang, Hui & Xiong, Momiao & Wang, Wei, 2008. "Symmetry-based structure entropy of complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(11), pages 2611-2619.
    2. Benguigui, L. & Porat, I., 2018. "Relationships between centrality measures of networks with diameter 2," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 243-251.
    3. Wang, Bing & Tang, Huanwen & Guo, Chonghui & Xiu, Zhilong, 2006. "Entropy optimization of scale-free networks’ robustness to random failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 591-596.
    4. Sun, Shiwen & Liu, Zhongxin & Chen, Zengqiang & Yuan, Zhuzhi, 2007. "Error and attack tolerance of evolving networks with local preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 851-860.
    5. G. Paul & T. Tanizawa & S. Havlin & H. Stanley, 2004. "Optimization of robustness of complex networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 38(2), pages 187-191, March.
    6. Zhu, Peican & Wang, Xinyu & Li, Shudong & Guo, Yangming & Wang, Zhen, 2019. "Investigation of epidemic spreading process on multiplex networks by incorporating fatal properties," Applied Mathematics and Computation, Elsevier, vol. 359(C), pages 512-524.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Milena Oehlers & Benjamin Fabian, 2021. "Graph Metrics for Network Robustness—A Survey," Mathematics, MDPI, vol. 9(8), pages 1-48, April.
    2. Vodák, Rostislav & Bíl, Michal & Sedoník, Jiří, 2015. "Network robustness and random processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 368-382.
    3. Dong, Gaogao & Tian, Lixin & Du, Ruijin & Fu, Min & Stanley, H. Eugene, 2014. "Analysis of percolation behaviors of clustered networks with partial support–dependence relations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 394(C), pages 370-378.
    4. Singh, Priti & Chakraborty, Abhishek & Manoj, B.S., 2017. "Link Influence Entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 701-713.
    5. Quayle, A.P. & Siddiqui, A.S. & Jones, S.J.M., 2006. "Preferential network perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 823-840.
    6. Wang, Jin-Shan & Wu, Yong-Ping & Li, Li & Sun, Gui-Quan, 2020. "Effect of mobility and predator switching on the dynamical behavior of a predator-prey model," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    7. Deng, Ye & Wu, Jun & Tan, Yue-jin, 2016. "Optimal attack strategy of complex networks based on tabu search," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 74-81.
    8. Wu, Jiang & Zuo, Renxian & He, Chaocheng & Xiong, Hang & Zhao, Kang & Hu, Zhongyi, 2022. "The effect of information literacy heterogeneity on epidemic spreading in information and epidemic coupled multiplex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    9. Hu, Menglong & Wang, Juan & Kong, Lingcong & An, Kang & Bi, Tao & Guo, Baohong & Dong, Enzeng, 2015. "Incorporating the information from direct and indirect neighbors into fitness evaluation enhances the cooperation in the social dilemmas," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 47-52.
    10. Porat, Idan & Benguigui, Lucien, 2021. "Global migration topology analysis and modeling of directed flow network 2006–2010," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    11. Gagan Matta, 2020. "Science communication as a preventative tool in the COVID19 pandemic," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-14, December.
    12. Yang, Zhirou & Liu, Jing, 2018. "A memetic algorithm for determining the nodal attacks with minimum cost on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1041-1053.
    13. Zanin, Massimiliano, 2015. "Can we neglect the multi-layer structure of functional networks?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 184-192.
    14. Zhou, Baoquan & Han, Bingtao & Jiang, Daqing & Hayat, Tasawar & Alsaedi, Ahmed, 2021. "Ergodic stationary distribution and extinction of a hybrid stochastic SEQIHR epidemic model with media coverage, quarantine strategies and pre-existing immunity under discrete Markov switching," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    15. Chen, Yahong & Huang, He, 2022. "Modeling the impacts of contact tracing on an epidemic with asymptomatic infection," Applied Mathematics and Computation, Elsevier, vol. 416(C).
    16. Wang, Xinyu & Jia, Danyang & Gao, Shupeng & Xia, Chengyi & Li, Xuelong & Wang, Zhen, 2020. "Vaccination behavior by coupling the epidemic spreading with the human decision under the game theory," Applied Mathematics and Computation, Elsevier, vol. 380(C).
    17. Billio, Monica & Casarin, Roberto & Costola, Michele & Pasqualini, Andrea, 2016. "An entropy-based early warning indicator for systemic risk," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 45(C), pages 42-59.
    18. Zhou, Baoquan & Jiang, Daqing & Han, Bingtao & Hayat, Tasawar, 2022. "Threshold dynamics and density function of a stochastic epidemic model with media coverage and mean-reverting Ornstein–Uhlenbeck process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 196(C), pages 15-44.
    19. Jalili, Mahdi, 2011. "Error and attack tolerance of small-worldness in complex networks," Journal of Informetrics, Elsevier, vol. 5(3), pages 422-430.
    20. You, Xuemei & Zhang, Man & Ma, Yinghong & Tan, Jipeng & Liu, Zhiyuan, 2023. "Impact of higher-order interactions and individual emotional heterogeneity on information-disease coupled dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:366:y:2020:i:c:s0096300319306289. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.