融合多策略的蜣螂算法的三维无人机航迹规划OA
Three-dimensional UAV path planning based on Dung Beetle Algorithm fused with multiple strategies
针对蜣螂算法在无人机航迹规划中存在的种群多样性匮乏、收敛迟缓和全局探索力不足等问题,提出了一种融合多策略的更优蜣螂优化算法(BDBO).该算法将动态学习的佳点集初始化、基于时间选择的主动并行双边搜索、自然衰减种群机制及融合Sigmoid的边界收敛策略嵌入航迹规划全过程:在全局阶段迅速扩大可行航迹空间,在局部阶段精细调整航迹最优点,从而同步提升收敛速度与求解精度.以航迹长度、飞行安全、平滑度及高度代价的加权和为目标函数,在CEC2017及三维山地环境进行验证.结果表明:BDBO所得航迹的适应度值较对比的四种算法平均降低19.47％、25.51％、20.85％和3.97％,充分证明了 BDBO在无人机三维航迹规划中的有效性与优越性.
To address the deficiencies of the Dung Beetle Optimizer(DBO)in UAV path planning-namely,insufficient population diversity,slow convergence,and weak global exploration,a multi-strategy-enhanced variant termed BDBO is proposed.The algorithm seamlessly integrates four synergistic components throughout the entire planning process:dynamic-learning-based good-point set initialization,time-triggered active parallel bilateral search,a natural population-decay mechanism,and a boundary convergence strategy fused with a Sigmoid function.Collectively,these strategies rapidly expand the feasible flight-corridor during the global phase and refine waypoint positions in the local phase,thereby accelerating convergence and improving solution accuracy simultaneously.A composite objective function aggregating path length,flight safety,trajectory smoothness,and altitude cost is minimized on both CEC2017 benchmarks and a realistic 3-D mountainous scenario.Experimental results reveal that BDBO reduces the best fitness value by 19.47％,25.51％,20.85％,and 3.97％compared with four state-of-the-art counterparts,unequivocally demonstrating its effectiveness and superiority for three-dimensional UAV path planning.
李尧;黄大庆;徐文校;赵喆
南京航空航天大学 电子信息工程学院,江苏 南京 210016南京航空航天大学 电子信息工程学院,江苏 南京 210016南京航空航天大学 电子信息工程学院,江苏 南京 210016南京航空航天大学 电子信息工程学院,江苏 南京 210016
信息技术与安全科学
蜣螂算法主动并行双边搜索自然衰减种群优化法选择边界收敛策略
Dung Beetle algorithmactive parallel bilateral searchnatural decay population optimizationselected boundary convergence strategy
《中南民族大学学报（自然科学版）》 2026 (4)
540-547,8
中国高校产学研创新基金资助项目(2021ZYA04004)
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