Ship Weather Routing Based on Hybrid Genetic Algorithm Under Complicated Sea Conditions

Considering the effects of increased economic globalization and global warming,developing methods for reducing shipping costs and greenhouse gas emissions in ocean transportation has become crucial.Owing to its key role in modern navigation technology,ship weather routing is the research focus of several scholars in this field.This study presents a hybrid genetic algorithm for the design of an optimal ship route for safe transoceanic navigation under complicated sea conditions.On the basis of the basic genetic algorithm,simulated annealing algorithm is introduced to enhance its local search ability and avoid premature convergence,with the ship’s voyage time and fuel consumption as optimization goals.Then,a mathematical model of ship weather routing is developed based on the grid system.A measure of fitness calibration is proposed,which can change the selection pressure of the algorithm as the population evolves.In addition,a hybrid crossover operator is proposed to enhance the ability to find the optimal solution and accelerate the convergence speed of the algorithm.Finally,a multi-population technique is applied to improve the robustness of the algorithm using different evolutionary strategies.

Application of Improved Multi-Objective Ant Colony Optimization Algorithm in Ship Weather Routing

This paper presents a novel intelligent and effective method based on an improved ant colony optimization(ACO)algorithm to solve the multi-objective ship weather routing optimization problem,considering the navigation safety,fuel consumption,and sailing time.Here the improvement of the ACO algorithm is mainly reflected in two aspects.First,to make the classical ACO algorithm more suitable for long-distance ship weather routing and plan a smoother route,the basic parameters of the algorithm are improved,and new control factors are introduced.Second,to improve the situation of too few Pareto non-dominated solutions generated by the algorithm for solving multi-objective problems,the related operations of crossover,recombination,and mutation in the genetic algorithm are introduced in the improved ACO algorithm.The final simulation results prove the effectiveness of the improved algorithm in solving multi-objective weather routing optimization problems.In addition,the black-box model method was used to study the ship fuel consumption during a voyage;the model was constructed based on an artificial neural network.The parameters of the neural network model were refined repeatedly through the historical navigation data of the test ship,and then the trained black-box model was used to predict the future fuel consumption of the test ship.Compared with other fuel consumption calculation methods,the black-box model method showed higher accuracy and applicability.

Multi-Objective Weather Routing Algorithm for Ships Based on Hybrid Particle Swarm Optimization

Maritime transportation has become an important part of the international trade system.To promote its sustainable de-velopment,it is necessary to reduce the fuel consumption of ships,decrease navigation risks,and shorten the navigation time.Ac-cordingly,planning a multi-objective route for ships is an effective way to achieve these goals.In this paper,we propose a multi-ob-jective optimal ship weather routing system framework.Based on this framework,a ship route model,ship fuel consumption model,and navigation risk model are established,and a non-dominated sorting and multi-objective ship weather routing algorithm based on particle swarm optimization is proposed.To fasten the convergence of the algorithm and improve the diversity of route solutions,a mutation operation and an elite selection operation are introduced in the algorithm.Based on the Pareto optimal front and Pareto optimal solution set obtained by the algorithm,a recommended route selection criterion is designed.Finally,two sets of simulated navigation simulation experiments on a container ship are conducted.The experimental results show that the proposed multi-objective optimal weather routing system can be used to plan a ship route with low navigation risk,short navigation time,and low fuel consumption,fulfilling the safety,efficiency,and economic goals.

Research on Weather Routing Based on the Mobile Internet

The combination and application of the mobile internet techniques with the weather radar monitoring data and the numerical weather pre-diction data were introduced, and the smart phone weather routing application software for both land and aquatic traffic safety, which is equipped with the function of analysis and warning of disastrous weather, was developed to reduce potential weather risks encountered during the journey as much as possible.

T_GRASP: Optimization Algorithm of Ship Avoiding Typhoon Route

AGRASP-based algorithm called T_GRASP for avoiding typhoon route optimization is suggested to increase the security and effectiveness of ship navigation.One of the worst natural calamities that can disrupt a ship’s navigation and result in numerous safety mishaps is a typhoon.Currently,the captains manually review the collected weather data and steer clear of typhoons using their navigational expertise.The distribution of heavy winds andwaves produced by the typhoon also changes dynamically as a result of the surrounding large-scale air pressure distribution,which significantly enhances the challenge of the captain’s preparation for avoiding typhoon navigation.It is now necessary to find a solution to the challenge of designing a highsafety and effective ship navigation path to avoid typhoons.The T_GRASP algorithm is suggested to optimize the candidate set’s structure based on the GRASP algorithm’s frame.The algorithm can guarantee the safety of the ship to avoid typhoons and assure high route efficiency by using the lowest risk function,the shortest sailing time,and the least fuel consumption as the objective functions and the ship speed and highest safety as the model constraints.The outcomes of the simulation demonstrate the superiority of the suggested T_GRASP algorithm over both the conventional A∗algorithm and the ant colony algorithm.In addition to addressing issues with the traditional A∗algorithm,like its wide search space and poor efficiency,the proposed algorithm also addresses issues with the ant colony algorithm,like its excessive iterations and sluggish convergence.

Ship speed power performance under relative wind profiles in relation to sensor fault detection

Statistical data analysis and visualization approaches to identify ship speed power performance under relative wind(i.e.apparent wind)profiles are considered in this study.Ship performance and navigation data of a selected vessel are analyzed,where various data anomalies,i.e.sensor related erroneous data conditions,are identified.Those erroneous data conditions are investigated and several approaches to isolate such situations are also presented by considering appropriate data visualization methods.Then,the cleaned data are used to derive various relationships among ship performance and navigation parameters that have been visualized in this study,appropriately.The results show that the wind profiles along ship routes can be used to evaluate vessel performance and navigation conditions by assuming the respective sea states relate to their wind conditions.Hence,the results are useful to derive appropriate mathematical models that represent ship performance and navigation conditions.Such mathematical models can be used for weather routing type applications(i.e.voyage planning),where the respective weather forecast can be used to derive optimal ship routes to improve vessel performance and reduce fuel consumption.This study presents not only an overview of statistical data analysis of ship performance and navigation data but also the respective challenges in data anomalies(i.e.erroneous data intervals and sensor faults)due to onboard sensors and data handling systems.Furthermore,the respective solutions to such challenges in data quality have also been presented by considering data visualization approaches.