## 1 Introduction
The framework of the decision support method is shown in Figure 1. The navigation of super-large vessels within ports involves the utilization of advanced navigational equipment, such as the Automatic Identification System (AIS), Automatic Radar Plotting Aid (ARPA), and GPS. These tools enable the acquisition of crucial information regarding the vessel's encounters with other target vessels. During this process, the ship's pilot assesses whether an encounter or an emergency situation is unfolding based on the preliminary encounter situation, which relies on the calculated Distance to Closest Point of Approach (\(d_{CPA}\)) and Time to Closest Point of Approach (\(t_{CPA}\)) (in Section 2.2). In the event of an encounter, the collision risk index (CRI) is determined from the encounter information using the CRI sub-model (in Section 3.1). This sub-model takes into account both the ship domain and maneuvering characteristics of the super-large vessel (in Section 2.1). The encounter situation itself can be divided into two distinct phases: the free action phase and the urgent situation phase. The CRI is compared against a threshold of 0.5 to classify the encounter situation. If the CRI is below 0.5, normal navigation can proceed, and no immediate collision avoidance measures are required. However, ongoing real-time assessment of the encounter situation remains essential. Conversely, if the CRI exceeds 0.5, an urgent situation may arise, necessitating the prompt implementation of appropriate collision avoidance operations. This work introduces a speed-varying collision avoidance decision support model, which combines the CRI sub-model and a speed-varying energy-loss sub-model (in Section 3.3). By incorporating the requirements of COLREGS as constraints for speed-varying collision avoidance, the model ensures that collision avoidance decisions \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Authors**} & **Collision** & \multirow{2}{*}{**Applied Waters**} & **Motion** & \multirow{2}{*}{**COLR-EGS**} \\  & & **Avoidance Operation** & & & **Characteristics** & \\ \hline GA & [PERSON] et al. [8; 9] & steering & open water & med & yes \\ ACO & Lazarovska [13; 14] & steering & open water & high & yes \\ NNs & Shen et al. [18] & steering & open water & high & yes \\ PSO & Zheng et al. [22] & steering & open water & med & yes \\ BAS & Xie et al. [24] & steering & open water & med & yes \\ DIA & Xu [25] & steering & open water & med & yes \\ CSO & Wei et al. [26] & steering & open water & low & yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of vessel collision avoidance methods based on heuristic algorithms. are both safety-oriented and economically optimal (in Section 3.2). The model function is used as an adaptation of CIPSO (in Section 4). Through an iterative optimization process facilitated by CIPSO, safe speed-varying collision avoidance decision values can be derived. Eventually, collision avoidance decision support can be provided to ship pilots to ensure the safety of super-large vessels navigating in the port. In conclusion, this work aims to enhance the safety and efficiency of maritime traffic in port waters by providing an effective collision avoidance decision support method that assists port managers and vessel operators in making accurate collision avoidance decisions in complex water environments. The main contributions of this work are as follows:  
1. A method is proposed for effective collision avoidance in port waters, considering the maneuvering characteristics and compliance with COLREGS regulations for super-large vessels. The method's suitability and effectiveness are demonstrated in multi-vessel collision avoidance scenarios within ports. 2. A vessel CRI assessment model is developed using the comprehensive judgment theory of fuzzy mathematics.