Global Path Planning For AUV Based on Improved Grey Wolf Optimization

Due to the gray wolf optimization algorithm's slow convergence rate and vulnerability to local optima, an enhanced version tailored for AUV path planning is introduced. Firstly, underwater obstacles and ocean current models are established based on the underwater environment, and a fitness function with path length, obstacle constraints, and time constraints is designed for the AUV. Additionally, a nonlinear convergence factor is devised, and the positional update concept from particle swarm optimization is incorporated to expedite the algorithm's convergence rate. Lastly, a Levy flight strategy is incorporated to safeguard against the algorithm's entrapment in local optima. Through simulation experiments, the proposed algorithm is compared with other algorithms. Simulation results indicate that the proposed algorithm exhibits accelerated convergence and proficiently identifies optimal solutions.