基于改进T分布烟花-粒子群算法的AUV全局路径规划

刘志华; 张冉; 郝梦男; 安凯晨; 陈嘉兴

doi:10.12263/DZXB.20230814

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基于改进T分布烟花-粒子群算法的AUV全局路径规划

学术论文 | 更新时间：2025-12-08

- 基于改进T分布烟花-粒子群算法的AUV全局路径规划
- AUV Global Path Panning Based on Improved T-Distribution Fireworks-Particle Swarm Optimization Algorithm
- 电子学报 2024年52卷第9期页码：3123-3134
- 作者机构：
  
  1.河北师范大学计算机与网络空间安全学院，河北石家庄 050024
  2.河北师范大学河北省网络与信息安全重点实验室，河北石家庄 050024
  3.河北师范大学河北省供应链大数据分析与数据安全工程研究中心，河北石家庄 050024
  4.河北师范大学数学科学学院，河北石家庄 050024
  5.河北正定师范高等专科学校，河北石家庄 050800
- 作者简介：
  
  [ "刘志华女，1977年4月出生于河北省沧州市.现为河北师范大学计算机与网络空间安全学院教授，硕士生导师.目前研究方向为水声传感器网络协同定位与追踪、水下机器人航迹规划、自主水下机器人协同控制等. E-mail: liuzhihua@hebtu.edu.cn" ]
  [ "张冉女，1998年7月出生于河北省任丘市.现为河北师范大学计算机与网络空间安全学院硕士研究生.主要研究方向为水声网络导航智能算法设计、水下机器人全局路径规划. E-mail: zhangran07012022@163.com" ]
  [ "郝梦男女，2000年3月出生于河北省石家庄市.现为河北师范大学计算机与网络空间安全学院硕士研究生.主要研究方向为水下机器人路径规划算法设计、水下机器人群智计算航迹规划. E-mail: mnhao0318@163.com" ]
  [ "安凯晨男，2001年4月出生于河北省廊坊市.现为河北师范大学计算机与网络空间安全学院硕士研究生.主要研究方向为水声网络定位导航算法设计.E-mail: ankaichen2023@163.com" ]
  [ "陈嘉兴男，1977年1月出生于天津市.教授，博士生导师.主要研究方向为水下机器人导航、水下机器人协同编队与路径规划、群智计算等.中国电子学会会员编号：E190004383S.E-mail: chenjx@hebtu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62171179;61771181)
- DOI：10.12263/DZXB.20230814
  中图分类号： TP242.6
- 收稿：2023-08-26，
  
  修回：2024-01-06，
  
  纸质出版：2024-09-25
- 稿件说明：
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刘志华, 张冉, 郝梦男, 等. 基于改进T分布烟花-粒子群算法的AUV全局路径规划[J]. 电子学报, 2024, 52(09): 3123-3134.

LIU Zhi-hua, ZHANG Ran, HAO Meng-nan, et al. AUV Global Path Panning Based on Improved T-Distribution Fireworks-Particle Swarm Optimization Algorithm[J]. Acta Electronica Sinica, 2024, 52(09): 3123-3134.
刘志华, 张冉, 郝梦男, 等. 基于改进T分布烟花-粒子群算法的AUV全局路径规划[J]. 电子学报, 2024, 52(09): 3123-3134. DOI：10.12263/DZXB.20230814

LIU Zhi-hua, ZHANG Ran, HAO Meng-nan, et al. AUV Global Path Panning Based on Improved T-Distribution Fireworks-Particle Swarm Optimization Algorithm[J]. Acta Electronica Sinica, 2024, 52(09): 3123-3134. DOI：10.12263/DZXB.20230814

摘要

针对传统粒子群算法在处理自主水下机器人（Autonomous Underwater Vehicle，AUV）全局路径规划时面临的寻优时间长、能耗高的问题，本文提出一种改进的T分布烟花-粒子群算法（T-distribution Fireworks-Particle Swarm Optimization Algorithm，TFWA-PSO），该算法融合了烟花算法的高效全局搜索能力和粒子群算法的快速局部寻优特性.在变异阶段，提出自适应T分布变异来扩大搜索范围，并在理论上证明了该变异方式能够使个体在局部最优解附近增强搜索能力.在选择阶段提出了适应度选择策略，淘汰适应度差的个体，解决了传统烟花算法易丢失优秀个体的问题，并对改进的T分布烟花算法与传统烟花算法的收敛速度进行对比.将改进算法的爆炸操作、变异操作和选择策略融合到粒子群算法中，对粒子群算法的速度更新公式进行了改进，同时从理论上对所改进的算法进行了收敛性证明.仿真实验结果表明，TFWA-PSO能够有效规划出一条最短路径，同时与给定的智能优化算法相比，TFWA-PSO在寻找最优路径的时间上平均降低了24.72%，能耗平均降低了17.33%，路径长度平均降低了16.96%.

Abstract

In response to the long optimization time and high energy consumption faced by traditional particle swarm optimization algorithm in global path planning for autonomous underwater vehicle

this paper proposes an improved T-distribution fireworks-particle swarm optimization algorithm (TFWA-PSO)

this algorithm integrates the efficient global search capability of the fireworks algorithm with the rapid local optimization characteristics of the particle swarm optimization algorithm. In the mutation stage

an adaptive T-distribution mutation is proposed to expand the search range

and it is theoretically demonstrated that this explosive mutation approach enables individuals to enhance their search ability near the local optimal solution. In the selection stage

a fitness selection strategy is proposed to eliminate individuals with poor fitness

solving the problem of the traditional fireworks algorithm's tendency to lose excellent individuals

and comparing the convergence speed between the improved T-distribution fireworks algorithm and the traditional fireworks algorithm. The improved algorithm's explosion

mutation operations

and selection strategy are integrated into the particle swarm algorithm. The velocity update formula of the particle swarm algorithm is improved

while the convergence proof of the improved algorithm is proved theoretically. The simulation results indicate that the TFWA-PSO can effectively plan the shortest path. Compared to the given intelligent optimization algorithms

TFWA-PSO on average reduces the time to find the optimal path by 24.72%

lowers energy consumption by 17.33%

and decreases the average path length by 16.96%.

关键词

Keywords

references

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