An Enhanced Gold Rush Optimizer for USV Path Planning in Complex Environments

To address the problems of slow convergence, long planned paths, and excessive turning points in unmanned surface vehicle (USV) path planning under complex environments, this paper proposes a path planning method based on an Enhanced Gold Rush Optimizer (EGRO). A nonlinear adaptive parameter adjustment strategy and a stage-wise dynamic probability mechanism are designed to improve the balance between global exploration and local exploitation at different stages of iteration. In addition, a Gaussian diffusion mechanism combined with a local search operator is introduced to enhance the algorithm’s ability to escape from local optima and reduce the number of path turning points. In the remote-sensing-image-based sea-ice simulation scenario, compared with the conventional GRO, PSO, and GWO algorithms, the maximum observed improvements of EGRO in best fitness, convergence iterations, and the number of path turning points are approximately 31.65%, 51.24%, and 35.00%, respectively. The simulation results indicate that EGRO can provide a feasible swarm-intelligence-based optimization framework for USV path planning. The proposed algorithm can generate feasible paths with relatively shorter lengths and fewer turning points. These characteristics may provide a favorable geometric reference for subsequent trajectory generation and navigation control, thereby highlighting the potential value of EGRO in engineering applications of USV path planning.