Research on the Deep Learning Technology in the Hull Form Optimization Problem

Abstract

A high−accuracy objective function evaluation method is crucial in ship hull form optimization. This study proposes a novel approximate ship hull form optimization framework using the deep learning technology, deep belief network algorithm. To illustrate the advantages of using the deep belief network algorithm in the prediction of total resistance, two traditional surrogate models (ELMAN and RBF neural networks) are also employed in this study to predict total resistance for different modified ship models. It can be seen from the results that the deep belief network algorithm is more suitable for forecasting total resistance of a DTMB5512 ship model than the traditional surrogate models. Following this, two design variables are selected to alter the bow geometry of the DTMB5512 ship model. The total resistance for different modified ship hulls is estimated using the deep belief network algorithm. Furthermore, an optimal solution with minimum total resistance in a two−dimensional space is obtained using the particle swarm optimization algorithm. The optimization results indicate that the optimization framework using the deep belief network algorithm can obtain an optimal solution with the smallest total resistance for different ship speeds.