With the rapid development of virtual reality (VR) technology, this paper proposes an access point (AP) correlation method based on VR user behavior awareness to address the problem of how current AP correlation methods only focus on the performance improvements of ordinary users and ignore the impact of VR user behavior on service quality. This paper analyzes the AP association method under the coverage scenario of a multi-access point (multi-AP) scenario environment and controls the performance improvement of VR user APs or APs under the access controller (AC) by association. Firstly, the VR network application scenario and system model were constructed, and secondly, the user behavior was sensed by analyzing the viewing habits of users. Then, the VR user association problem based on VR user behavior perception was transformed into a “many-to-many” matching problem between VR user devices and APs, and the generalized multidimensional multiple choice knapsack (GMMKP) model was established to solve the problem using the backpack problem theory; the suboptimal solution algorithm was selected to obtain the best VR user AP association strategy. The experimental results show by simulation that the proposed algorithm in this paper performed better in terms of the AP load balancing and average network download latency compared to the comparison algorithms.

Maritime transportation is the backbone of global trade, but containerships face severe safety risks of container loss due to synchronous rolling and parametric rolling under complex sea conditions. To address this issue, this study proposes a containership route optimization method integrating these two critical safety constraints. First, ensemble forecasting is adopted to characterize the uncertainty of wave conditions, and safety constraints for synchronous rolling and parametric rolling are constructed based on the Conditional Value-at-Risk (CVaR) framework, with the optimization objective of minimizing the total voyage cost including fuel consumption cost and time cost. Second, the Ant Colony Optimization (ACO) algorithm is employed with a rolling-aware enhancement strategy, which involves initializing pheromones via a normal distribution around the reference route, constructing a heuristic matrix that integrates distance, speed and rolling risk, and introducing an elite ant mechanism to balance global exploration and local exploitation. A case study on the North Atlantic route verifies the proposed approach against the Genetic Algorithm (GA) and A* algorithm. Results show that the ACO based approach achieves optimal comprehensive performance, with average speed reaching 14 kt, 5.26% higher than GA and 3.70% higher than A*; total voyage cost is reduced by 8.2% compared to GA and 16% compared to A*. Meanwhile, it effectively avoids high-risk rolling zones through coordinated adjustments of heading and speed, ensuring navigational safety. This research provides reliable decision support for the safe and economical operation of containerships.

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.