With the advantages of flexible control ability, unmanned surface vehicle (USV) has been widely applied in civil and military fields. A number of researchers have been working on the development of intelligent path planning algorithms to plan a high-quality and collision-free path which is applied to guide USV through cluttered environments. The conventional algorithms may either have issues with trapping into a local optimal solution or face a slow convergence problem. This paper presents a novel multi-subpopulation bacterial foraging optimisation (MS-BFO) algorithm for USV path planning that enhances the searching performance, especially, in a complex environment. This method constructs the deletion and immigration strategies (DIS), which guarantees the elite optimised individual of each subpopulation to be inherited by others, thus to consequently lead to fast convergence speed. The experimental results show that the proposed method is able to suggest an optimised path within the shortest length of time, compared with other optimisation algorithms.

无人水面车辆（USV）具有控制能力灵活的优点，已在民用和军事领域得到了广泛的应用。许多研究人员一直在致力于智能路径规划算法的开发，以规划高质量且无碰撞的路径，该路径可用于指导USV穿越混乱的环境。常规算法可能存在陷入局部最优解的问题，或者面临缓慢的收敛问题。本文提出了一种新颖的用于USV路径规划的多亚群细菌觅食优化（MS-BFO）算法，该算法可增强搜索性能，尤其是在复杂环境中。此方法构造了删除和移民策略（DIS），从而确保每个子种群的精英优化个体都可以被其他人继承，因此导致快速收敛。实验结果表明，与其他优化算法相比，该方法能够在最短的时间内提出优化路径。