GCLW and NSGA-Ⅲ-based multi-source dynamic collaborative optimization for wind-assisted ship energy consumption

Optimizing ship energy efficiency (SEE) is crucial for the shipping industry to reduce emissions. Existing methods are limited to static, single-dimensional optimization of routes or speeds, lacking multi-source dynamic collaborative approaches, especially for wind-assisted ships. Therefore, this paper proposes a multi-source collaborative optimization method for the energy efficiency of wind-assisted ships. The coupling effects among sailing speed, sail's angle of attack (AOA), sailing route, and time-varying environmental variables are integrated into a unified framework. This proposed method dynamically optimizes route, speed, and sail's AOA by integrating the NSGA-III algorithm with a fuel consumption model that combines a GA-CNN-LSTM-WBM (GCLW). Case study shows that the proposed method can reduce fuel consumption and CO 2 emissions by 7.34%. Compared with static collaborative optimization, the dynamic method can further improve energy efficiency by 1.19%. This method is of great significance for further improving the energy efficiency of wind-assisted ships and hence promoting the shipping decarbonization.