Ship Hull Form Optimization by Evolutionary Algorithm


This thesis examines the application of Evolutionary Algorithms for ship hull form optimization. In addition the thesis briefly examines the use of an Artificial Neural Network for predicting a particular hull attribute. A method is developed where different chromosomes are combined to model the hull. A matrix chromosome is used to model the hull offsets. A one-dimensional array or single chromosome is used to model the principal parameters. The method allows both optimization of the principal parameters to obtain an initial design satisfying the requirements as well as concurrent optimization of the hull form to minimize resistance and maximize other performance attributes of seakeeping and stability. Hull form optimization is conducted using hydrodynamic evaluations of key performance attributes. In particular, performance in terms of ship resistance, seakeeping and stability are evaluated. The design methodology uses multiple objective optimizations and a novel multiple objective optimization technique is developed. The search for potential designs uses evolutionary algorithms to optimize both the hull form and determine the principal parameters satisfying the design requirements. A multi-species genetic algorithm is developed to enable competition between alternate hull forms. In order to obtain a reasonable approximation of the resistance, some modifications of classical linearised thin-ship theory is utilized. A particular problem for vessels with low length to beam ratio and with transom sterns is investigated. In additional to resistance the candidates are evaluated in terms of seakeeping performance. Seakeeping is evaluated using a hydrodynamic evaluation of the hull forms in a regular seaway. A two dimensional strip theory analysis of seakeeping provides the input to develop a vertical motion seakeeping index. With respect to stability an analysis of vessel candidates is conducted using a regression based formulation and an artificial neural network. A database of typical candidates is required to provide data on vessel attributes that are required for training the neural network. The determination of the center of gravity or KG is then used as input for the seakeeping evaluation as well as to satisfy the constraint for a maximum KG.