Ship Weather Routing Based on Hybrid Genetic Algorithm Under Complicated Sea Conditions

Considering the effects of increased economic globalization and global warming, developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial. Owing to its key role in modern navigation technology, ship weather routing is the research focus of several scholars in this field. This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions. On the basis of the basic genetic algorithm, simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence, with the ship's voyage time and fuel consumption as optimization goals. Then, a mathematical model of ship weather routing is developed based on the grid system. A measure of fitness calibration is proposed, which can change the selection pressure of the algorithm as the population evolves. In addition, a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm. Finally, a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.