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This study proposes an Improved Artificial Hummingbird Algorithm (IAHA), which enhances the convergence performance of the traditional AHA through chaotic initialization, dynamic probability adjustments for guided and territorial foraging, and improvements to the migratory foraging strategy. For the task assignment problem of heterogeneous UAV localization, comparative simulation experiments with AHA, Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Butterfly Optimization Algorithm (BOA) demonstrate the superiority of IAHA in heterogeneous UAV localization task assignment. The results provide a new and effective optimization algorithm for solving similar task assignment problems.

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Research on Positioning Task Assignment Technology for Heterogeneous UAVs Based on Improved Artificial Hummingbird Algorithm

  • 会议论文

  • 发表日期：

    2026年

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  • Xiangyu Sun,
  • Longmeng Ji,
  • Jiandong Zhang,
  • Guoqing Shi,
  • Qiming Yang,
  • Yaozhong Zhang

摘要

This study proposes an Improved Artificial Hummingbird Algorithm (IAHA), which enhances the convergence performance of the traditional AHA through chaotic initialization, dynamic probability adjustments for guided and territorial foraging, and improvements to the migratory foraging strategy. For the task assignment problem of heterogeneous UAV localization, comparative simulation experiments with AHA, Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and Butterfly Optimization Algorithm (BOA) demonstrate the superiority of IAHA in heterogeneous UAV localization task assignment. The results provide a new and effective optimization algorithm for solving similar task assignment problems.