Speed optimization based on BP artificial neural network and genetic algorithm
In order to further improve the energy efficiency of ships,a speed optimization technology route based on BP artificial neural network and genetic algorithm is proposed.First of all,some methods for constructing fuel consumption models are briefly introduced.Secondly,the BP artificial neural network was used to establish the fuel consumption model of the target ship,and the average absolute error of the model prediction was 2.3％,and the accuracy and generalization ability basically met the requirements of engineering applications.Finally,genetic algorithm is used to optimize the segmented speed of the target ship based on historical meteorological data.The calculation results show that the sailing time of the tar-get ship can not only be reduced by 1.35 days,but also save fuel loss by 10.1％after speed optimization,which indicates that segmented speed optimization of sailing ships is a feasible solution.
BP neural networkgenetic algorithmfuel consumption modelspeed optimization
万方数据
维普
