Multi-objective ship weather routing method based on the improved NSGA-III algorithm

This study investigates a multi-objective weather routing problem for ships, aiming to minimize operational costs, sailing time, and CO2 emissions simultaneously. Departing from conventional graph search algorithms based on nodes, we incorporate the ship's operational context, utilizing heading angle and speed as direct variables. Employing the Non-Dominated Sorting Genetic Algorithm III (NSGA-III) to solve the above problem, we observed convergence difficulties due to the traditional random initial population strategy. To address this, an improved NSGA-III is proposed, generating the initial population based on a route from a combination of sampling and the A* algorithm. The proposed method is applied to routes in the North Atlantic and the North Pacific, revealing that, compared to the commonly used A* and 3D Dijkstra methods, it can achieve over a 1.5 % reduction in ship operating costs, surpassing 6 % in certain scenarios. Additionally, we conducted an analysis of set coverage and inverted generational distance. The findings indicate that the improved NSGA-III algorithm exhibits superior convergence and diversity compared to the traditional NSGA-III algorithm.