As one of the key technologies for the safe navigation of ships, intelligent collision avoidance decision-making is of great significance for the development of intelligent ships. Aiming at the intelligent collision avoidance decision-making problem under multi-vessel encounters, an improved chaos sparrow search optimization algorithm (CSSOA) based on Gaussian variation and Tent chaos is proposed.

The algorithm uses Tent chaotic mapping to initialize the original sparrow population and improve its diversity, chaotic mapping is applied to sparrows with poor adaptability and stagnant search ability, and Gaussian mutation is used to improve the local search ability and robustness. The improved scheme optimizes the problems of heuristic algorithms such as slow convergence speed and tendency to fall into the local optimum. A collision risk model is established using the fuzzy membership function with the comprehensive consideration of the ship-to-ship speed ratio, minimum encounter distance, relative distance, minimum encounter time and relative orientation.

In a typical encounter scenario involving multiple ships, the experimental results demonstrate that the average number of iterations for the improved algorithm is reduced by 77.97% and 53.57% compared to particle swarm optimization and the original sparrow algorithm respectively.

The improved CSSOA can achieve a safer and more efficient collision avoidance path at a superior convergence speed, providing valuable guidance for ship navigators in making collision avoidance decisions.