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With developments in artificial intelligence, computer science, and network technologies, modern confrontational scenarios are increasingly related to multi-agent systems characterized by multi-network. To reduce the effects of external factors on task execution, a task migration algorithm based on link uncertainty in adversarial scenarios is designed. Firstly, this thesis models the underlying problem as an integer programming problem, defining the optimization objectives and constraints accordingly. Subsequently, inter- and intra-network layer task migration is carried out based on gene expression programming to ensure the optimization objective and improve the load balance. Finally, experimental results demonstrate the algorithm’s efficacy in improving migration benefits and mitigating the impact of uncertainty.

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Research on Task Migration Problem Based on Link Uncertainty in Adversarial Scenarios

  • 会议论文

  • 发表日期：

    2025年

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  • Xiangxiang Xing,
  • Pan Li,
  • Tianyu Zuo,
  • Yuanshuang Jiang,
  • Kai Di,
  • Xin Wang,
  • Xun Jiao,
  • Yichuan Jiang,
  • Dan Chen

摘要

With developments in artificial intelligence, computer science, and network technologies, modern confrontational scenarios are increasingly related to multi-agent systems characterized by multi-network. To reduce the effects of external factors on task execution, a task migration algorithm based on link uncertainty in adversarial scenarios is designed. Firstly, this thesis models the underlying problem as an integer programming problem, defining the optimization objectives and constraints accordingly. Subsequently, inter- and intra-network layer task migration is carried out based on gene expression programming to ensure the optimization objective and improve the load balance. Finally, experimental results demonstrate the algorithm’s efficacy in improving migration benefits and mitigating the impact of uncertainty.