多策略改进蜣螂优化算法的无人机航迹规划
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1.广西民族大学物理与电子信息学院 南宁 530006;2.广西民族大学人工智能学院 南宁 530006

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TP18;TN96

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广西科学与技术项目(AD23023001)、广西科技基地和人才专项(桂科AD22080021)资助


Multi-strategy improved dung beetle optimization algorithm for UAV path planning
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1.College of Physics & Electronic Information, Guangxi Minzu University,Nanning 530006, China; 2.School of Artificial Intelligence, Guangxi Minzu University, Nanning 530006, China

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    摘要:

    针对蜣螂优化算法存在陷入局部最优、全局搜索能力不足致使无人机三维航迹规划效果不佳的问题,设计了一种多策略改进的蜣螂优化算法。通过构建三维空间模型,结合路径长度、威胁、高度和平滑度等因素,构建了综合评价函数。首先,采用混合混沌序列提升初始种群多样性;其次,在蜣螂滚球阶段引入“差分变异”算子以提升算法的局部搜索能力,并结合改进的正弦算法,通过概率切换机制进行个体更新,进一步提升算法的全局搜索性能;最后,在繁殖阶段引入了改进的螺旋搜索策略,增强算法跳出局部最优的能力。通过对6个基准函数的优化对比分析并展示粒子在搜索空间中的运动轨迹,结果表明改进后的算法在收敛速度、精确度和鲁棒性方面表现更优。将算法应用于三维无人机路径规划中,路径长度的最优值、平均值和最差值分别提升了0.41%、5.67%和18.03%,进一步验证了改进策略的有效性以及该算法在处理实际工程应用中的优越性。

    Abstract:

    To address the issues in the dung beetle optimization algorithm, such as falling into local optima and insufficient global search capability, which lead to suboptimal performance in 3D UAV path planning, a multi-strategy improved dung beetle optimization algorithm was designed. A 3D spatial model was constructed, and a comprehensive evaluation function was developed by considering factors such as path length, threat, altitude, and smoothness. First, a hybrid chaotic sequence was employed to enhance the initial population diversity. Then, during the dung beetle rolling stage, a “differential mutation” operator was introduced to improve the algorithm′s local search ability. This was combined with an improved sine algorithm to update individuals via a probability switching mechanism, further enhancing the global search capability. Finally, an improved spiral search strategy was incorporated during the breeding stage to strengthen the algorithm′s ability to escape local optima. Through optimization of six benchmark functions and analysis of particle motion trajectories in the search space, the results demonstrated that the improved algorithm performed better in terms of convergence speed, accuracy, and robustness. When applied to 3D UAV path planning, the optimal, average, and worst values of path length improved by 0.41%, 5.67%, and 18.03%, respectively, further validating the effectiveness of the improvement strategies and the superiority of this algorithm in practical engineering applications.

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梅雨琳,曲良东,饶爽.多策略改进蜣螂优化算法的无人机航迹规划[J].电子测量技术,2025,48(11):67-77

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  • 在线发布日期: 2025-07-07
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