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针对无人机三维航迹规划寻优问题，提出一种改进的人工蜂群(Artificial Bee Colony, ABC)算法。通过引入淘汰策略提升采蜜蜂全局搜索能力；结合粒子群算法自我认知和社会认知思想优化新蜜源产生方式，提升算法局部开发能力；将无效开采数量作为新的影响因素更新跟随蜂蜜源选择概率，进一步提升算法效率。在构建的三维任务场景数字地图上的多项实验结果表明，所提算法精度高、收敛速度快，具有良好的航迹规划寻优效果。
Abstract：An improved Artificial Bee Colony(ABC) algorithm is proposed for the optimization problem of 3D trajectory planning of unmanned aerial vehicles. The global search ability of foraging bees is enhanced by introducing an elimination strategy. The new nectar source generation is optimized by combining the self-cognition and social cognition ideas of particle swarm algorithm to enhancing the local development ability of the algorithm. In addition, in order to further improve the efficiency of the algorithm, the invalid mining quantity is used as a new influencing factor to update the nectar source selection probability of the following bees. Multiple experimental results on the constructed 3D scene digital map show that the proposed algorithm has high accuracy, fast convergence speed, and good trajectory planning optimization effect.
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基本信息:
DOI：
中图分类号:TP18;V279;V249
引用信息:
[1]韩子硕,张莉,范喜全等.基于改进人工蜂群算法的无人机三维航迹规划[J].无线电工程,2025,55(01):196-203.
基金信息: