Research on Multi-Objective Ship Speed Optimization Based on Evolutionary Deep Learning

The maritime industry faces the urgent challenge of reducing greenhouse gas (GHG) emissions while maintaining economic viability, especially under the International Maritime Organization’s (IMO) Net-Zero Framework and Carbon Intensity Indicator (CII). Optimizing ship speed is a key operational measure, but it involves a complex trade-off between fuel consumption, voyage time, and regulatory compliance costs. This paper presents a multi-objective ship speed optimization method using Evolutionary Deep Learning (EDL). In this study, EDL is defined as the integration of a deep gradient boosting fuel predictor (CatBoost) and a gradient-free evolutionary optimizer (Natural Evolution Strategies, NES). A hybrid fuel consumption prediction model combines ISO 15016:2015 physical constraints with CatBoost, achieving a Mean Absolute Percentage Error of 6.45%. The optimization model minimizes total operating costs and GHG emissions, incorporating Greenhouse Gas Fuel Intensity (GFI) compliance costs, CII rating constraints, and a voyage segmentation strategy. The problem is solved with an NES algorithm using Gaussian population representation and an elitism strategy. A case study of a transpacific voyage of a large container vessel (COSCO PACIFIC) shows that the proposed EDL method achieves the lowest GHG emissions among all benchmark algorithms (reducing CO2eq by 9.18% compared to NSGA-II) and the fastest computation time (63.9% shorter than NSGA-II). While MOPSO and MOACO yield lower raw fuel costs by sacrificing emissions and compliance performance, EDL attains a superior balance across all objectives—emissions, compliance costs, and Comprehensive Fitness—with robust convergence and high computational efficiency. This approach offers practical support for sustainable ship navigation under complex regulatory pressures.