为了解决传统蚁群算法收敛速度慢，易陷入局部最优，传统粒子群算法搜索精度差，初始路径不规则等问题，提出一种融合了改进蚁群算法（IACO）、改进粒子群算法（IPSO）和遗传算法（GA）的IACO-GA-IPSO路径规划算法。首先定义三维海洋环境模型，将工作空间沿Z轴方向划分成水平的栅格平面；其次建立多标准的路径优劣评价模型；最后由融合算法规划路径：IACO算法生成次优种群，GA算法优化种群多样性，IPSO算法快速收敛到全局最优。实验结果表明，融合算法能充分发挥每种算法的优点，克服种群规模和收敛速度的矛盾，优化初始种群，提高全局搜索能力、局部搜索精度和算法运行效率，加快收敛速度并避免陷入局部最优路径。
To overcome challenges faced by conventional ant colony and particle swarm optimization algorithms, an IACO-GA-IPSO path planning algorithm is proposed, which integrates the improved ant colony algorithm (IACO), improved particle swarm algorithm (IPSO), and genetic algorithm (GA). Firstly, a 3D marine environment model is defined, with the workspace divided into horizontal grid planes along the Z-axis. Secondly, A multi-standard path evaluation model is established. Finally, the fusion algorithm generates paths: the IACO algorithm generates a suboptimal population, the GA algorithm optimizes population diversity, and the IPSO algorithm quickly converges to the global optimum. The experimental results show that the fusion algorithm can fully leverage the advantages of each algorithm, overcome the contradiction between population size and convergence speed, optimize the initial population, improve global search ability, local search accuracy, and algorithm operation efficiency, accelerate convergence speed, and avoid falling into local optimal paths.
2024,46(18): 99-105 收稿日期：2023-10-30
DOI：10.3404/j.issn.1672-7649.2024.18.017
分类号：TP301.6
作者简介：刘新宇(1999-),男,硕士研究生,研究方向为船舶与海洋结构物设计制造
参考文献：
[1] 宋保维, 潘光, 张立川, 等. 自主水下航行器发展趋势及关键技术[J]. 中国舰船研究, 2022, 17(5): 27-44.
SONG Baowei, PAN Guang, ZHANG Lichuan, et al. Development trend and key technologies of autonomous underwater vehicles[J]. Chinese Journal of Ship Research, 2022, 17(5): 27-44.
[2] 侯海平, 付春龙, 赵楠, 等. 智能自主式水下航行器技术发展研究[J]. 舰船科学技术, 2022, 44(1): 86-90.
HOU Haiping, FU Chunlong, ZHAO Nan, et al. Research on technology development of the intelligent AUV[J]. Ship Science and Technology, 2022, 44(1): 86-90.
[3] 张楠楠, 姜文刚, 窦刚. 改进蚁群算法在AUV三维路径规划中的研究[J]. 计算机工程与应用, 2019, 55(11): 265-270.
[4] 张骁, 韩云涛, 许振, 等. 基于Dubins曲线和遗传算法的AUV路径规划方法[J]. 自动化技术与应用, 2020, 39(12): 7-10+100.
[5] 朱佳莹, 高茂庭. 融合粒子群与改进蚁群算法的AUV路径规划算法[J]. 计算机工程与应用, 2021, 57(6): 267-273.
[6] 董志洋, 李慧, 葛靖宇, 等. 改进蚁群算法的无人机三维环境路径规划[J]. 测绘通报, 2023(5): 153-157.
[7] 周翔. 大尺度环境下的AUV路径规划与路径跟踪[D]. 青岛: 中国海洋大学, 2010.
[8] 刘利强, 于飞, 戴运桃. 基于蚁群算法的水下潜器三维空间路径规划[J]. 系统仿真学报, 2008(14): 3712-3716.
[9] 黄书召, 田军委, 乔路, 等. 基于改进遗传算法的无人机路径规划[J]. 计算机应用, 2021, 41(2): 390-397.
