A hybrid approach for forecasting ship motion using CNN–GRU–AM and GCWOA

Abstract

The motion of a ship, which has six degrees of freedom, is a complex nonlinear dynamic process with variable periodicity and chaotic characteristics. With the development of smart ships, modern high-precision equipment needs the help from high accuracy of ship motion (SHM) forecasting. Existing models will not easily be able to satisfy future accuracy requirements. Therefore, to improve the accuracy of SHM forecasts, by firstly determining the sequence features of SHM time series, a convolutional neural network (CNN) was used herein to extract automatically spatial feature vectors. Considering the variable-period characteristics of SHM time series, a gated recurrent unit (GRU) was used to learn the inherent time characteristics and to extract temporal feature vectors. The attention mechanism (AM) was developed to control the effect of feature vectors on the output to solve the problem of the contribution of feature vectors. Integrating the above methods, an SHM hybrid forecasting model, the SHM CNN–GRU–AM (SHM-C&G&A) model, was established. Secondly, in view of the difficulty of selecting the hyperparameters of a hybrid model, on account of the defects of the whale optimization algorithm (WOA), a normal cloud local search (NCLS) algorithm was developed. Exploiting the advantages of the normal cloud search (NCS) and the genetic algorithm (GA), a genetic random global search (GRGS) algorithm was developed. Then, a hybrid genetic cloud whale optimization algorithm (GCWOA) was developed and used to optimize the hyperparameters of the SHM-C&G&A model. Accordingly, a hybrid forecasting approach that integrates SHM-C&G&A and GCWOA was proposed; it is referred to as GCWOA-SHM-C&G&A. Finally, ship heave and pitch time series data are used to analyze an example to test the forecasting effectiveness of SHM-C&G&A and the optimization performance of GCWOA. The experimental results reveal that the proposed SHM-C&G&A model is more robust that the other models that are considered in this paper, and exhibits better nonlinear characteristics. The proposed GCWOA yields a better combination of hyperparameters than contrast algorithms in the forecasting process.