[1]汪果果,白艺杰,柴梦娟,等.基于蝴蝶优化改进算法的无人机三维路径规划[J].郑州大学学报(工学版),2026,47(3):57-66.[doi:10.13705/j.issn.1671-6833.2026.03.005]
　WANG Guoguo,BAI Yijie,CHAI Mengjuan,et al.Based on Butterfly Optimization Improvement Algorithm for UAVs 3D Path Planning[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):57-66.[doi:10.13705/j.issn.1671-6833.2026.03.005]
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基于蝴蝶优化改进算法的无人机三维路径规划()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年3期
页码:
57-66
栏目:
出版日期:
2026-05-27

文章信息/Info

Title:
Based on Butterfly Optimization Improvement Algorithm for UAVs 3D Path Planning
文章编号:
1671-6833(2026)03-0057-10
作者:
汪果果1,2, 白艺杰2, 柴梦娟2, 余道杰2, 王怡澄2
1.郑州大学 网络空间安全学院,河南 郑州 450001;2.信息工程大学 信息系统工程学院,河南 郑州 450001
Author(s):
WANG Guoguo1,2, BAI Yijie2, CHAI Mengjuan2, YU Daojie2, WANG Yicheng2
1.School of Cyberspace Security, Zhengzhou University, Zhengzhou 450001, China; 2.School of Information System Engineering, Information Engineering University, Zhengzhou 450001, China
关键词:
蝴蝶优化算法;  动态窗口法;  Tent混沌映射;  反向学习;  路径规划
Keywords:
butterfly optimization algorithm;  dynamic window approach;  tent chaotic mapping;  inverse learning;  path planning
分类号:
TP18:TP301
DOI:
10.13705/j.issn.1671-6833.2026.03.005
文献标志码:
A
摘要:
针对无人机在多重威胁环境下路径规划复杂度高、难以在有效时间内生成高质量路径的问题,提出一种多策略融合的粒子群-蝴蝶优化改进算法(IPSOBOA)。通过Tent混沌映射结合反向学习策略优化初始种群,增强种群多样性;引入非线性参数调整和动态转换概率机制,平衡全局搜索与局部开发;结合粒子群算法,在局部搜索阶段引入速度项,提出在速度动态变化的位置更新方程,提升搜索效率。分别基于三维静态和动态环境下不同威胁场景,将IPSOBOA与蝴蝶优化算法及其他多种优化算法进行对比。实验结果表明:IPSOBOA在静态环境的3种场景下相较于蝴蝶优化算法,最优适应度值分别优化1.8%、17%和44%,路径长度分别优化1.8%、42.4%和61.3%;在动态环境下实现全局路径跟踪与实时避障的结合,生成更平滑、安全性更高的路径。
Abstract:
Aiming at the problem of high complexity of path planning and difficulty in generating high-quality paths in effective time for UAVs in complex threat environments, a multi-strategy fusion particle swarm-butterfly optimization improvement algorithm (IPSOBOA) was proposed. The initial population was optimized through tent chaotic mapping combined with inverse learning strategy to enhance the diversity of the population; nonlinear parameter adjustment and dynamic conversion probability mechanism were introduced to balance the global search and local exploitation; combined with the particle swarm algorithm, the velocity term was introduced in the local search phase, and the position update equation with dynamic change of velocity was proposed to improve the search efficiency. Based on four benchmark test functions and three different threat scenarios respectively, IPSOBOA was compared with the butterfly optimization algorithm and various other optimization algorithms. The experimental results showed that, compared with the butterfly optimization algorithm in three scenarios of the static environment, IPSOBOA optimized the optimal fitness value by 1.8%, 17%, and 44% respectively, and optimized the path length by 1.8%, 42.4%, and 61.3% respectively; in the dynamic environment, it combined global path tracking and real-time obstacle avoidance to generate smoother and safer paths.

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更新日期/Last Update: 2026-05-27