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<p>The overlapping coalition formation (OCF) game has emerged as a key framework in multi-agent systems, enabling unmanned aerial vehicles (UAVs) to participate in multiple distinct coalitions simultaneously. This paper studies constrained task allocation in heterogeneous UAV systems using OCF, with a focus on resource heterogeneity, communication constraints, and temporal limitations. We introduce an adaptive exploration–exploitation mechanism that systematically refines the solution space while avoiding premature convergence in coalition optimization. Comparative simulations with state-of-the-art OCF methods and variable neighborhood search (VNS) demonstrate that our approach significantly improves performance in resource-limited settings, achieving a 24.57% increase in coalition utility and a 38.46% higher task completion rate compared to conventional coalition formation algorithms, with a slight increase in convergence time and coalition size. These results quantitatively confirm the effectiveness of the proposed method in balancing exploration–exploitation trade-offs for coalition optimization.</p>

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Task-oriented overlapping coalition formation: a game-theoretic framework for heterogeneous UAV task allocation

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Shaokun Yan,
  • Jingxiang Feng,
  • Peng Chang

摘要

The overlapping coalition formation (OCF) game has emerged as a key framework in multi-agent systems, enabling unmanned aerial vehicles (UAVs) to participate in multiple distinct coalitions simultaneously. This paper studies constrained task allocation in heterogeneous UAV systems using OCF, with a focus on resource heterogeneity, communication constraints, and temporal limitations. We introduce an adaptive exploration–exploitation mechanism that systematically refines the solution space while avoiding premature convergence in coalition optimization. Comparative simulations with state-of-the-art OCF methods and variable neighborhood search (VNS) demonstrate that our approach significantly improves performance in resource-limited settings, achieving a 24.57% increase in coalition utility and a 38.46% higher task completion rate compared to conventional coalition formation algorithms, with a slight increase in convergence time and coalition size. These results quantitatively confirm the effectiveness of the proposed method in balancing exploration–exploitation trade-offs for coalition optimization.