文献类型：期刊文献

中文题名：基于改进蚁群遗传算法的无人艇最短航路径规划

英文题名：Shortest-Voyage-Time Path Planning of Unmanned Surface Vehicle Based on Modified Ant Colony Genetic Algorithm

作者：孙蕴菲[1];仉天宇[1,2,3,4,5];尹建川[1];黄应邦[2,6];张峻萍[2];林汛[2]

机构：[1]广东海洋大学船舶与海运学院,广东湛江524088;[2]广东海洋大学海洋与气象学院,广东湛江524088;[3]广东海洋大学近海海洋变化与灾害预警实验室,广东湛江524088;[4]广东海洋大学广东省高等学校陆架及深远海气候资源与环境重点实验室,广东湛江524088;[5]自然资源部空间海洋遥感与应用重点实验室,北京100081;[6]中国水产科学研究院南海水产研究所,广州510300

年份：2025

卷号：47

期号：6

起止页码：92

中文期刊名：船舶工程

外文期刊名：Ship Engineering

收录：北大核心2023、、北大核心

基金：国家重点研发计划(2021YFC3101801);国家重点研发计划(2022YFC3103104);国家自然科学基金面上项目(52271361,42476219,41976200);南方海洋实验室(珠海)项目(SML2022SP301);广东省教育厅创新团队项目(2023KCXTD015);广东省科技计划项目(粤西海洋野外站);广东海洋大学人才启动项目(060302032106)。

语种：中文

中文关键词：无人艇;路径规划;改进蚁群算法;遗传算法;最短航时路径

外文关键词：unmanned surface vehicle;path planning;modified ant colony optimization;genetic algorithm;shortest-voyage-time path

中文摘要：[目的]为实现无人艇在万山群岛内以最短航行时间完成多航点巡航任务,提出一种基于改进后的时间蚁群遗传算法(T-ACOGA)最短航时路径规划方法。[方法]引入时间启发因子,将蚁群算法寻优目的改为路径航时,并控制信息素的增量。随后融合改进后的时间蚁群算法(T-ACO)和遗传算法(GA),将每代最优路径作为GA的初始种群,从而克服GA生成初始种群的盲目性。考虑风对无人艇速度的影响,构建由路径航时和路径平滑度组成的T-ACOGA适应度函数,平滑函数值为路径所有节点角度对应惩罚值之和。[结果]无风情况下,相比于基本蚁群算法和T-ACO,T-ACOGA路径航时分别减少近7.66%和6.74%;有风情况下,相比于T-ACO,T-ACOGA路径航时减少近11.345%,并且在有风或无风的情况下,T-ACOGA均能够提高80%以上的路径平滑值,[结论]说明该算法规划的路径航时更短且更平滑,有利于提高无人艇航行效率。

外文摘要：[Purpose]To realize the multi-point cruise task of the unmanned surface vehicle(USV)in Wanshan Islands in the shortest voyage time,a shortest-voyage-path planning method based on a modified temporal ant colony genetic algorithm(T-ACOGA)is proposed.[Method]A time heuristic factor is introduced,the optimization objective of ant colony algorithm is changed to path navigation time,and controlling the increment of pheromone.The modified temporal ant colony optimization(T-ACO)is integrated with genetic algorithm(GA),using the optimal path of each generation as the initial population of GA,thereby overcoming the blindness in generating the initial population in GA.Considering the influence of wind on USV speed,a T-ACOGA fitness function is constructed,which comprises path navigation time and path smoothness,where the smoothness function value is the sum of the penalty values corresponding to the angles of all nodes in the path.[Result]In calm conditions,compared to basic ACO and T-ACO,the path voyage time of T-ACOGA is reduced by nearly 7.66%and 6.74%,respectively.In windy conditions,compared to T-ACO,T-ACOGA reduces the path voyage time by nearly 11.345%,additionally,under both windy and calm conditions,T-ACOGA can increase the path smoothness value by over 80%.[Conclusion]It shows that the algorithm plans shorter and smoother paths,which is conducive to improving the navigation efficiency of USVs.