In the downturn of the shipping industry, optimizing the speed of ships sailing on fixed routes has important practical significance for reducing operating costs. Based on the ship-engine-propeller matching relationship, this paper uses BP neural network to build main engine power model, and correction factors are introduced into the main engine power model to reflect the influence of wind and wave. The Kalman filter algorithm is used to filter the data collected by a river-sea direct ship during the voyage from Zhoushan to Zhangjiagang. The filtered data and the meteorological data obtained from the European Medium-Range Weather Forecast Center are used as the data set of the BP neural network to predict the main engine power. Based on the main engine power model, a multi-objective optimization model of ship speed under the influence of actual wind and waves was established to solve the conflicting goals of reducing sailing time and reducing main engine fuel consumption. This multi-objective model is solved by a non-dominated fast sorting multi-objective genetic algorithm to obtain the Pareto optimal solution set, thereby obtaining the optimal speed optimization scheme. Compared with the original navigation scheme, the navigation time is reduced by 8.83%, and the fuel consumption of the main engine is reduced by 12.95%. The results show that the optimization model can effectively reduce the fuel consumption and control the sailing time, which verifies the effectiveness of the algorithm.

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