This paper uses the data collected during the voyage of a 20000-ton bulk carrier from Zhoushan to Zhangjiagang and the meteorologicaldata obtained from the ECMWF as a data set. Based on the correlationanalysis results of the data set, the ship's engine fuel consumptionmodel is established based on BP neural network. As the neuralnetwork training progresses, the parameters of the ship's engine fuelconsumption model will be corrected continuously. In order to solvethe problem of conflict between the two objectives of reducing thenavigation time of the ship and reducing the fuel consumption of theship's engine, this paper divides the navigation area and the navigationtime by adding the time axis in the square grid diagram, and establishesa multi-objective optimization model for ship routes under theinfluence of actual wind waves. The multi-objective model is solved byan adaptive niche genetic algorithm to obtain the Pareto optimalsolution set, thereby obtaining the optimal route scheme.
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