Research on collision avoidance decision-making of hybrid optimization algorithm based on k and t index

To address the situation where ship pilots may encounter ineffective collision avoidance or even ship collisions during the process of maneuvering ships, leading to economic losses, marine pollution, and even threats to human safety, a ship decision -making based on a hybrid optimization algorithm is proposed. The hybrid optimization algorithm includes two optimization processes, genetic algorithm (GA) and fmincon. This research is based on Nomoto model to establish a ship paths model utilizing the ship's turning ability index K and the tracking ability index T. The constraint function ensures safety navigation between ships and the objective function ensures the minimum loss of voyage throughout the entire collision avoidance process. Hybrid optimization algorithm ensures the achievement of global minimum values and computes the decision data including steering time and steering angle, which can assist ship drivers to make a more reasonable decision. The results of this research are of great significance for improving navigation safety and provides reference and support for the future development of intelligent navigation technology.