Research And Simulation Of Multi-objective Ship Weather Routing Optimization Algorithm

The world shipping industry continues to grow and develop with the increasingly close trade exchanges between countries around the world and the acceleration of economic globalization and regional integration.Ships carry the vast majority of goods in world trade,and ocean transportation has become the most important mode of transportation in international trade.It is the basic requirement of the ship's maritime navigation to maximize the safety of the ship.On this basis,economic benefit is also a matter that must be taken into account when choosing a ship's route.In order to ensure the safety,economy and punctuality of ships at sea.In this paper,an intelligent and effective method for optimizing meteorological routes is proposed from the aspects of ship navigation safety,fuel consumption and sailing time.Firstly,a comprehensive overview of the multi-target route optimization problem is presented.The innovation of this paper is mainly reflected in the improvement of traditional ant colony algorithm.In order to find the optimal route of transoceanic ships quickly,efficiently and accurately,the article improves the algorithm from five aspects,which are briefly described below:First of all,the rules of ant movement are changed to make ants more purposeful in searching path points.Secondly,the relevant parameters in traditional ant colony optimization algorithm are optimized to make the algorithm more suitable for ship route optimization at sea.Thirdly,new control factors are added to constantly revise the route.Fourthly,the pheromone updating rules are improved to improve the convergence speed of the algorithm and avoid local optimum.Fifthly,the crossover,recombination and mutation operations in genetic algorithm are added to improve the situation of too few Pareto non-dominant solutions for multi-objective problem solved by ant optimization colony algorithm.Next,the mathematical model of ship route optimization is established.In geographic coordinates,ship motion position is discretized in time.At the same time,the navigation map is rasterized and the static and dynamic constraints in the process of ship navigation are analyzed.On the basis of the above model,the optimization objectives of meteorological routes are analyzed,and the optimization objective functions are set to correspond to each optimization objective respectively.By comparing the values of the objective functions of the solutions,the Pareto non-dominant relationship between feasible solutions is determined.Then,build a fuel consumption black box model based on measured data.In this paper,artificial neural network(ANN)is used to construct the fuel consumption black box model and use historical navigation data of the test vessel to train model parameters,the trained black box model is used to predict the future fuel consumption of test ships.This involves the analysis and processing of data and the construction and training of the ANN model.Finally,the experimental environment of the ship meteorological route is optimized and the experimental simulation results are analyzed and discussed.In this paper,the algorithm program is written by MATLAB software,and the M function file of the algorithm is converted into a C file by Coder to improve the running efficiency of the program.The results show that the improved ant colony optimization algorithm can solve the multi-objective problem well and ensure that the planned route achieves safe,economical and punctual navigation effect.