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<p>Three-dimensional path planning for unmanned aerial vehicles (UAVs) features high dimensionality and strong coupling of multiple constraints. Its solution space contains a large number of local optima, which urgently requires high-performance algorithms to achieve an efficient and stable solution. To address the above challenges, this paper proposes an improved algorithm named DSQRIME. DSQRIME optimizes the basic Rime Optimization Algorithm (RIME) in three aspects. First, it introduces differential evolution mutation to strengthen global exploration capability. Second, it uses spiral search to refine the hard-rime mechanism, helping the algorithm escape local optima. Third, it adopts quasi-oppositional-based learning to further improve solution accuracy and stability. Experiments are carried out on the IEEE CEC 2017 benchmark suite and in UAV path planning scenarios. The results verify that DSQRIME outperforms the original RIME and several state-of-the-art metaheuristic algorithms. The trajectories generated by DSQRIME are smoother and achieve lower path costs.</p>

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DSQRIME: an enhanced RIME algorithm with application to 3D UAV path planning

  • 期刊论文

  • 发表日期：

    2026年5月18日

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  • Chengcheng Chen,
  • Mingbin Wang,
  • Jiatong Liu,
  • Xianchang Wang,
  • Helong Yu,
  • Jiehong Wu,
  • Huiling Chen,
  • Jiajia Li,
  • Mingyue Zhou

摘要

Three-dimensional path planning for unmanned aerial vehicles (UAVs) features high dimensionality and strong coupling of multiple constraints. Its solution space contains a large number of local optima, which urgently requires high-performance algorithms to achieve an efficient and stable solution. To address the above challenges, this paper proposes an improved algorithm named DSQRIME. DSQRIME optimizes the basic Rime Optimization Algorithm (RIME) in three aspects. First, it introduces differential evolution mutation to strengthen global exploration capability. Second, it uses spiral search to refine the hard-rime mechanism, helping the algorithm escape local optima. Third, it adopts quasi-oppositional-based learning to further improve solution accuracy and stability. Experiments are carried out on the IEEE CEC 2017 benchmark suite and in UAV path planning scenarios. The results verify that DSQRIME outperforms the original RIME and several state-of-the-art metaheuristic algorithms. The trajectories generated by DSQRIME are smoother and achieve lower path costs.