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MPMOPs in WSNs are challenging as they require balancing conflicting objectives of multiple decision - makers. This paper presents an enhanced MPIA. It innovates in three ways: a dynamic MCM threshold adapting with iteration, an improved cross - party guidance for balanced Pareto front coverage, and an adaptive operator parameter adjustment based on success rates. Experiments on WSN routing optimization show it outperforms the original MPIA, NSGA - II, DEMOPSO and NSGA - III. It achieves a 459% hypervolume improvement over the original MPIA and excels across metrics, validating its effectiveness in WSN multi - party optimization.

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An Enhanced Multi-party Immune Algorithm with Adaptive Mechanisms for Wireless Sensor Network Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Shu-Chuan Chu,
  • YaYu Zhang,
  • Pei Hu,
  • Hao Shu,
  • Min Liu

摘要

MPMOPs in WSNs are challenging as they require balancing conflicting objectives of multiple decision - makers. This paper presents an enhanced MPIA. It innovates in three ways: a dynamic MCM threshold adapting with iteration, an improved cross - party guidance for balanced Pareto front coverage, and an adaptive operator parameter adjustment based on success rates. Experiments on WSN routing optimization show it outperforms the original MPIA, NSGA - II, DEMOPSO and NSGA - III. It achieves a 459% hypervolume improvement over the original MPIA and excels across metrics, validating its effectiveness in WSN multi - party optimization.