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Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security

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  • Jianpeng Zhang
  • Hai Wang
  • Xueli Wang

Abstract

In order to optimize the spectrum allocation strategy of existing wireless communication networks and improve information transmission efficiency and data transmission security, this study uses the independent correlation characteristics of chaotic time series to simulate the collection and control strategy of bees, and proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection control. Furthermore, it proposes an artificial bee colony algorithm based on uniform mapping and collaborative collection and control. The method begins by establishing a composite system of uniformly distributed Chebyshev maps. In the neighborhood intervals where the nectar sources are firmly connected and relatively independent, the algorithm then conducts a chaotic traversal search. The research results demonstrated the great performance of the suggested algorithm in each test function as well as the positive effects of the optimization search. The network throughput rate was over 300 kbps, the quantity of security service eavesdropping was below 0.1, and the spectrum utilization rate of the algorithm-based allocation method could be enhanced to 0.8 at the most. Overall, the performance of the proposed algorithm outperformed the comparison algorithm, with high optimization accuracy and a significant amount of optimization. This is favorable for the efficient use of spectrum resources and the secure transmission of communication data, and it encourages the development of spectrum allocation technology in wireless communication networks.

Suggested Citation

  • Jianpeng Zhang & Hai Wang & Xueli Wang, 2024. "Application of artificial bee colony algorithm based on homogenization mapping and collaborative acquisition control in network communication security," PLOS ONE, Public Library of Science, vol. 19(7), pages 1-26, July.
  • Handle: RePEc:plo:pone00:0306699
    DOI: 10.1371/journal.pone.0306699
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    1. Gaeithry Manoharam & Mohd Shareduwan Mohd Kasihmuddin & Siti Noor Farwina Mohamad Anwar Antony & Nurul Atiqah Romli & Nur ‘Afifah Rusdi & Suad Abdeen & Mohd. Asyraf Mansor, 2023. "Log-Linear-Based Logic Mining with Multi-Discrete Hopfield Neural Network," Mathematics, MDPI, vol. 11(9), pages 1-30, April.
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