GA-LSTM and NSGA-III based collaborative optimization of ship energy efficiency for low-carbon shipping

Abstract

Improving ship energy efficiency has been a crucial part for energy preservation and emission control of the shipping industry . However, the current optimization technologies mainly concentrate on a single optimization of navigation speed, route and trim. There is still a shortage of an effective collaborative optimization method to enhance the ship energy efficiency . In this regard, it is necessary to carry out more efficient optimization approach to further enhance the ship fuel efficiency. Therefore, a new collaborative optimization approach for energy efficiency optimization considering the coupling effects of navigation route, speed, trim and various environmental variables is proposed in this study. Firstly, a predictive model for ship energy consumption , which considers the sailing route, speed, trim and various environmental factors, is established by using Genetic Algorithm (GA) improved Long Short-Term Memory (LSTM) approach. On these bases, a collaborative optimization method based on the Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed. The results of a case study show that the proposed collaborative optimization strategy can save fuel consumption by as much as 4.54%, compared with the original operational mode. Therefore, it holds significant importance for further enhancing ship fuel efficiency and promoting the advancement of low-carbon shipping.