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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
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    References listed on IDEAS

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    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.
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