大地测量与地球动力学2025，Vol.45Issue(9)：922-928,936,8.DOI:10.14075/j.jgg.2024.10.484
一种重力匹配导航路径规划新算法
A New Algorithm for Gravity Matching Navigation Path Planning:Particle Swarm Optimization and Grey Wolf Optimization
摘要
Abstract
The accuracy of gravity matching navigation is influenced by the adaptability of the sailing sea area.To ensure the effectiveness of matching,it is crucial to conduct path planning for the sub-mersible.In response to the issues of low efficiency,susceptibility to local optimal solutions,and unstable performance of existing algorithms,this paper proposes a hybrid algorithm-particle swarm optimization and grey wolf optimization(PSO-GWO)to enhance the path planning efficiency of submersibles.Building on the traditional grey wolf optimizer(GWO),the proposed algorithm incorporates the mem-ory mechanism of the best position information of particle movement from particle swarm optimization(PSO),reconstructing the update functions for velocity and position,thereby improving the optimiza-tion capability and convergence speed of the GWO algorithm.Experiments were conducted using PSO-GWO,GWO,and PSO algorithms on a two-dimensional grid map discretized based on adaptability.The results show that compared with the GWO and PSO algorithms,the PSO-GWO algorithm re-duces the planned path length by 10.99％and the number of iterations by 82.43％.This algorithm re-duces redundant travel and accelerates convergence,significantly improving the underwater navigation efficiency of the submersible.关键词
重力匹配导航/路径规划/灰狼算法/粒子群算法/多属性决策Key words
gravity matching navigation/track planning/grey wolf optimization(GWO)/particle swarm optimization(PSO)/multi-attribute decision分类
天文与地球科学引用本文复制引用
孙玮萱,肖云,张锦柏,陈垲宁..一种重力匹配导航路径规划新算法[J].大地测量与地球动力学,2025,45(9):922-928,936,8.基金项目
国家自然科学基金(41374083). National Natural Science Foundation of China,No.41374083. （41374083）
