摘要
针对无人机三维航迹规划问题,提出一种改进型多目标水母搜索算法(MOJS算法).该研究对经典MOJS算法进行了多项改进,包括基于SPM混沌映射的水母种群初始化策略、基于凸透镜成像反向学习的多样性提升策略、基于柯西逆累积分布算子的被动运动行为模式优化策略以及基于正切飞行算子的主动运动行为模式优化策略.仿真实验结果表明,改进型MOJS算法在航迹总长度、算法运行时间以及受威胁成本等多个评价指标上均优于经典MOJS算法.此外,文章还探讨了算法在未来工作中的改进空间,包括算法鲁棒性、实时性能、高目标优化、集成学习以及实际飞行测试.
The problem of 3D trajectory planning for unmanned aerial vehicles(UAVs)was addressed in this article by proposing an Improved Multi-Objective Jellyfish Search(MOJS)algorithm.The study included several improvements to the classic MOJS algorithm,such as the SPM chaotic mapping-based jellyfish population initialization strategy,the convex lens imaging-based reverse learning strategy for enhancing population diversity,the Cauchy inverse cumulative distribution operator-based passive movement behavior optimization strategy,and the tangent flight operator-based active movement behavior optimization strategy.Simulation results demonstrated the superiority of the improved MOJS algorithm over the classic MOJS algorithm in various performance metrics,including total trajectory length,algorithm running time,and threat cost.Furthermore,the potential areas for future work were discussed,including algorithm robustness,real-time performance,high-dimensional optimization,
作者
田疆
钱春燕
TIAN Jiang;QIAN Chunyan(Department of Transportation and Surveying,Gansu Jiaotong Vocational andTechnical College,Lanzhou 730207,China;Physical Education College,Northwest Minzu University Lanzhou 730124,China)
出处
《西北民族大学学报（自然科学版）》
2024年第4期41-55,共15页
Journal of Northwest Minzu University(Natural Science)
关键词
无人机
三维航迹规划
多目标优化
水母搜索算法
算法改进
Unmanned aerial vehicles
3D Trajectory planning
Multi-objective Optimization
Jellyfish search algorithm
Algorithm improvement
