火力与指挥控制2025，Vol.50Issue(2)：93-99,7.DOI:10.3969/j.issn.1002-0640.2025.02.013
基于融合改进人工鱼群算法的无人机航迹规划
Drone Trajectory Planning Based on Improved Artificial Fish Swarm Algorithm
摘要
Abstract
In response to the issues of low solution accuracy and difficult to escape from local optimal problems,etc.when applying the standard Artificial Fish Swarm Algorithm(AFSA)for trajectory planning in complex environments,an improved algorithm called the IAAFSA is proposed.Firstly,this algorithm incorporates a self-adaptive strategy for the fish swarm,the step size and crowding factor are dynamically adjusted to balance the early exploration capability with the later solution accuracy.Secondly,it introduces the social cognitive perspective from PSO to accelerate the convergence speed of the algorithm.Finally,during the crossbreeding of the elite strategy during the iterative solving process of fish swarm,the diversity of population is enhanced,thereby the algorithm's ability to find better solutions in global search is improved.In the experiment,MATLAB is used to simulate and validate the algorithm on two maps with different terrain characteristics.The results indicate that IAAFSA demonstrates better convergence speed and optimization accuracy when addressing three-dimensional trajectory planning problems.关键词
无人机/航迹规划/自适应人工鱼群算法/社会认知/精英策略/交叉繁衍Key words
UAV/trajectory planning/adaptive AFSA/social cognition/elite strategy/crossbreeding分类
航空航天引用本文复制引用
王璞,刘宏杰..基于融合改进人工鱼群算法的无人机航迹规划[J].火力与指挥控制,2025,50(2):93-99,7.
