Feature fusion with transformer-LSTM-A and NSGA-III for ship energy efficiency optimization in green shipping

Green and energy-efficient maritime transport has become a strategic imperative under tightening decarbonization mandates by the International Maritime Organization (IMO). However, current ship energy efficiency optimization (EEO) frameworks often decouple fuel consumption prediction from operational decision-making, limiting real-time adaptability and integrated control. To address this gap, this study proposes a high-resolution collaborative framework that couples a Transformer-LSTM-A prediction model with a voyage-segmented NSGA-III multi-objective optimizer. An NSGA-III solves the four-objective, segment-level speed-and-trim optimization subject to practical stability and operating limits. The proposed architecture incorporates temporal attention mechanisms and VMD-enhanced multivariate inputs to accurately forecast fuel consumption rates, which then guide the segment-wise optimization of ship speed and trim. The optimization simultaneously minimizes fuel consumption, CO 2 emissions, and the Energy Efficiency Operational Indicator (EEOI), while embedding soft constraints on voyage distance to preserve navigational feasibility. A real-world case study demonstrates the effectiveness of the proposed approach, achieving reductions of 4.76% in FCR, 3.04% in CO 2 emissions, and 1.50% in EEOI. These results validate the framework’s potential for intelligent maritime energy management, offering a robust and scalable pathway toward low-carbon ship operations aligned with global regulatory targets.