## 1 Introduction  
Along with the relentless development and global expansion of international trade, the shipping industry has assumed paramount significance as an essential and irreplaceable mode of transportation at a global scale. Simultaneously, ship and shipping technologies have undergone rapid and remarkable advancements, characterized by the discernible trend toward the construction of large-scale, high-speed, and specialized vessels [1]. Notably, the emergence of super-large vessels has solidified their position as a principal conduit in the realm of global trade. However, the challenges encountered by super-large vessels while navigating port waters have become increasingly conspicuous. Among these challenges, collision avoidance assumes critical importance. The problem of collision avoidance faced by super-large vessels navigating port waters is marked by distinctive characteristics that set it apart from similar challenges encountered by other vessels. Firstly, the adjustment of course and speed for super-large vessels, owing to their immense size and weight, necessitates a more protracted time frame [2]. Secondly, navigation through narrow waterways and port areas demands a heightened level of dexterity and precision in vessel handling. Lastly, the considerable inertia inherent in super-large vessels results in prolonged stopping or course alteration times, thus necessitating a longer reaction time for executing collision avoidance maneuvers [3]. Consequently, the problem of collision avoidance encountered by super-large vessels operating in port waters has emerged as a field of research deserving of profound attention and scholarly inquiry. With the development of intelligent algorithms such as Genetic Algorithm (GA) [4], Neural Networks (NNs) [5], Ant Colony Optimization (ACO) [6], and Particle Swarm Optimization (PSO) [7], many experts and scholars have applied heuristic algorithms to the study of vessel collision avoidance issues. [PERSON] et al. [8; 9] used GA to encode four elements, namely, collision avoidance time, collision avoidance steering angle, resumption time, and resumption angle, and evolved them generation by generation through selection, crossover, and mutation operations. The vessel information in the Electronic Chart Display and Information System (ECDIS) was analyzed and processed, i.e., the vessel information in the ECDIS was transformed into a navigational network diagram, and the optimal collision avoidance path was solved using GA. [PERSON] et al. [10], [PERSON] et al. [11] [PERSON] et al. [12] have also applied GA to this issue. ACO can provide safety and efficiency collision avoidance decision support for vessel pilots due to the advantages of their distributed computation and positive feedback mechanisms ([PERSON] [13; 14]). [PERSON] and [PERSON] [15] proposed an ACO-based collision avoidance path-planning method for vessels. The collision risk between vessels was first converted into the pheromone concentration of each path, and then the best collision avoidance path was gradually determined by simulating ants moving on the path with a high pheromone concentration. [PERSON] et al. [16] proposed a vessel collision avoidance path-planning method considering spatio-temporal interaction effects. By analyzing the spatio-temporal characteristics of vessel movements, an effective path-planning algorithm was designed to avoid vessel collisions. [PERSON] and [PERSON] [17] proposed an efficient path-planning algorithm that enables autonomous vessels to plan navigational paths quickly and safely in restricted waters. The method combined map data and environmental constraints with an optimization algorithm to achieve an efficient path search and planning. [PERSON] et al. [18] proposed a collision avoidance algorithm for multi-ship systems based on deep reinforcement learning.